- > > >
In time–frequency analysis, waveform signals are analyzed in both time and frequency domains to capture the progressive change of spectrum components with time (e.g., a transient or nonstationary signal such as music). 'Waveform's signal experts were very knowledgeable and helped me pick out the right antenna and I'm now getting double the download speed on my Verizon data card. I went from 1-2 bars of shaky signal to consistent four bars, and my download speeds have increased from a range of from 200 to 800 kbps.
- As well as blank rows (aka Spacer rows) and Time axis. Signal and Bus waveforms are drawn by you, segment by segment, whereas Auto-signal and Auto-bus waveforms are drawn all at once, as a repeated pattern. A diagram can also have.
- Note: Change the IP address in the generateagilentwaveform.m file to the IP address of the signal source. This MATLAB example has been tested on an N5182A MXG but should work on other Agilent signal sources including these MXG, ESG, and PSG models: E4428C,E4438C,N5181A,N5182A,N5183A,E8241A,E8244A,E8251A,E8254A,E8247C,E8257C, E8267C,E8257D.
Data Acquisition Categories
Software
Program
Amplifiers
Calculate FFTs with WinDaq Waveform Browser
Available for free with every DATAQ Instruments data acquisition device.
View the Advanced WWB Filters Video
To calculate an FFT (Fast Fourier Transform), just listen. The human ear automatically and involuntarily performs a calculation that takes the intellect years of mathematical education to accomplish. The ear formulates a transform by converting sound—the waves of pressure traveling over time and through the atmosphere—into a spectrum, a description of the sound as a series of volumes at distinct pitches. The brain then turns this information into perceived sound.
A similar conversion can be done using mathematical methods on the same sound waves or virtually any other fluctuating signal that varies with respect to time. The Fourier transform is the mathematical tool used to make this conversion. Simply stated, the Fourier transform converts waveform data in the time domain into the frequency domain. The Fourier transform accomplishes this by breaking down the original time-based waveform into a series of sinusoidal terms, each with a unique magnitude, frequency, and phase. This process, in effect, converts a waveform in the time domain that is difficult to describe mathematically into a more manageable series of sinusoidal functions that when added together, exactly reproduce the original waveform. Plotting the amplitude of each sinusoidal term versus its frequency creates a power spectrum, which is the response of the original waveform in the frequency domain. Figure 1 illustrates this time to frequency domain conversion concept.
The Fourier transform has become a powerful analytical tool in diverse fields of science. In some cases, the Fourier transform can provide a means of solving unwieldy equations that describe dynamic responses to electricity, heat or light. In other cases, it can identify the regular contributions to a fluctuating signal, thereby helping to make sense of observations in astronomy, medicine and chemistry. Perhaps because of its usefulness, the Fourier transform has been adapted for use on the personal computer. Algorithms have been developed to link the personal computer and its ability to evaluate large quantities of numbers with the Fourier transform to provide a personal computer-based solution to the representation of waveform data in the frequency domain. But what should you look for in Fourier analysis software? What makes one software package better than another in terms of features, flexibility, and accuracy? This application note will present and explain some of the elements of such software packages in an attempt to remove the mystery surrounding this powerful analytical tool.
Figure 1 — The Fourier transform illustrated
DATAQ Instruments' WinDaq Waveform Browser (WWB) playback software contains a Fourier transform algorithm that was the model for this application note and includes all elements of Fourier transformation discussed herein. All graphics and concepts presented in this note are also derived from the WWB Fourier transform utility.
A Trio of Transforms
Before computers, numerical calculation of a Fourier transform was a tremendously labor intensive task because such a large amount of arithmetic had to be performed with paper and pencil. These calculations became more practical as computers and programs were developed to implement new methods of Fourier analysis. One such method was developed in 1965 by James W. Cooley and John W. Tukey1 Their work led to the development of a program known as the fast Fourier transform. The fast Fourier transform (FFT) is a computationally efficient method of generating a Fourier transform. The main advantage of an FFT is speed, which it gets by decreasing the number of calculations needed to analyze a waveform. A disadvantage associated with the FFT is the restricted range of waveform data that can be transformed and the need to apply a window weighting function (to be defined) to the waveform to compensate for spectral leakage (also to be defined).
An alternative to the FFT is the discrete Fourier transform (DFT). The DFT allows you to precisely define the range over which the transform will be calculated, which eliminates the need to window. On the negative side, the DFT is computationally slower than the FFT.
The transformation from the time domain to the frequency domain is reversible. Once the power spectrum is displayed by one of the two previously mentioned transforms, the original signal can be reconstructed as a function of time by computing the inverse Fourier transform (IFT). Each of these transforms will be discussed individually in the following paragraphs to fill in missing background and to provide a yardstick for comparison among the various Fourier analysis software packages on the market.
Power Spectrum Generation Using the FFT
The FFT is just a faster implementation of the DFT. The FFT algorithm reduces an n-point Fourier transform to about
(n/2) log2 (n)
complex multiplications. For example, calculated directly, a DFT on 1,024 (i.e., 210) data points would require
n2 = 1,024 × 1,024 = 220 = 1,048,576
multiplications. The FFT algorithm reduces this to about
(n/2) log2 (n) = 512 × 10 = 5,120
multiplications, for a factor-of-200 improvement.
But the increase in speed comes at the cost of versatility. The FFT function automatically places some restrictions on the time series to be evaluated in order to generate a meaningful, accurate frequency response. Because the FFT function uses a base 2 logarithm by definition, it requires that the range or length of the time series to be evaluated contains a total number of data points precisely equal to a 2-to-the-nth-power number (e.g., 512, 1024, 2048, etc.). Therefore, with an FFT you can only evaluate a fixed length waveform containing 512 points, or 1024 points, or 2048 points, etc. For example, if your time series contains 1096 data points, you would only be able to evaluate 1024 of them at a time using an FFT since 1024 is the highest 2-to-the-nth-power that is less than 1096.
Because of this 2-to-the-nth-power limitation, an additional problem materializes. When a waveform is evaluated by an FFT, a section of the waveform becomes bounded to enclose 512 points, or 1024 points, etc. One of these boundaries also establishes a starting or reference point on the waveform that repeats after a definite interval, thus defining one complete cycle or period of the waveform. Any number of waveform periods and more importantly, partial waveform periods can exist between these boundaries. This is where the problem develops. The FFT function also requires that the time series to be evaluated is a commensurate periodic function, or in other words, the time series must contain a whole number of periods as shown in Figure 2a to generate an accurate frequency response. Obviously, the chances of a waveform containing a number of points equal to a 2-to-the-nth-power number and ending on a whole number of periods are slim at best, so something must be done to ensure an accurate representation in the frequency domain. Before we examine a way to ensure accuracy in the frequency domain, lets look closer at the whole/partial number of periods dilemma.
What would happen if an FFT was performed on a waveform that did not contain a whole number of periods as shown in Figure 2b?
Figure 2 — An example of waveform continuity versus discontinuity that avoids complicated mathematical explanation. (a) shows a best case, one-in-a-million waveform where the range of the FFT exactly contains a whole number of periods, starting with the waveforms mean value. This waveform possesses end-point continuity as shown in ( c), which means the resulting power spectrum will be accurate and no window need be applied. A more typical encounter is shown in (b), where the range of the FFT does not contain a whole number of periods. The discontinuity in the end-points of this waveform (d) means the resulting power spectrum will contain high frequency components not present in the input, requiring the application of a window to attenuate the discontinuity and improve accuracy.
Think of the length of waveform to be evaluated as a ring that has been uncoiled. If the ends of the uncoiled ring were joined back together to again form a ring, a waveform consisting of a whole number of periods would join together perfectly as shown in Figure 2c. However, a waveform consisting of a fractional number of periods would not join together perfectly without a gap between or an overlapping of the ends as shown in Figure 2d. Thus, the FFT would evaluate this waveform with the end-point error and generate a power spectrum containing false frequency components representative of the end-point mismatch. Consider the spectra shown in Figure 3. This figure shows the power spectrum of two sine waves of equal amplitude and frequency. However, the peak of the right power spectrum appears somewhat 'spread out'. This inaccuracy is the result of an FFT performed on a waveform that does not contain a whole number of periods. The spreading out or 'leakage' effect of the right power spectrum is due to energy being artificially generated by the discontinuity at the end points of the waveform.
Fortunately, a solution exists to minimize this leakage effect error and ensure accuracy in the frequency domain. Aside from the DFT (to be defined), the only solution is to multiply the time series by a window weighting function before the FFT is performed. Most window weighting functions (often referred to as just 'windows') attenuate the discontinuity by tapering the signal to zero at both ends of the window, as shown in Figure 5d. However, if your waveform has important information appearing at the ends of the window, it will be destroyed by the tapering. In this case, a solution other than a window must be sought. With the window approach, the periodically incorrect signal as processed by the FFT will have a smooth transition at the end points which results in a more accurate power spectrum representation. A number of windows exist. Each has different characteristics that make one window better than the others at separating spectral components near each other in frequency, or at isolating one spectral component that is much smaller than another, or whatever the task. Some popular windows (named after their inventors) are Hamming, Bartlett, Hanning, and Blackman. The Hamming window offers the familiar bell-shaped weighting function but does not bring the signal to zero at the edges of the window. The Hamming window produces a very good spectral peak, but features only fair spectral leakage reduction. The Bartlett window offers a triangular shaped weighting function that brings the signal to zero at the edges of the window. This window produces a good, sharp spectral peak and is good at reducing spectral leakage as well. The Hanning window offers a similar bell-shaped window (a good approximation to the shape of the Hanning window can be seen inFigure 5d) that also brings the signal to zero at the edges of the window. The Hanning window produces good spectral peak sharpness (as good as the Bartlett window), but the Hanning offers very good spectral leakage reduction (better than the Bartlett). The Blackman window offers a weighting function similar to the Hanning but narrower in shape. Because of the narrow shape, the Blackman window is the best at reducing spectral leakage, but the trade-off is only fair spectral peak sharpness. As Figure 4 illustrates, the choice of window function is an art. It depends upon your skill at manipulating the trade-offs between the various window constraints and also on what you want to get out of the power spectrum or its inverse. Obviously, a Fourier analysis software package that offers a choice of several windows is desirable to eliminate spectral leakage distortion inherent with the FFT.
In short, the FFT is a computationally fast way to generate a power spectrum based on a 2-to-the-nth-power data point section of waveform. This means that the number of points plotted in the power spectrum is not necessarily as many as was originally intended. The FFT also uses a window to minimize power spectrum distortion due to end-point discontinuity. However, this window may attenuate important information appearing on the edges of the time series to be evaluated and distort the results of an IFT operation (to be defined) as can be seen in Figure 5d. With these limitations inherent to the FFT, does the Fourier analysis software package you are considering offer a solution other than the FFT?
Another solution abandons windowing in favor of allowing the user to precisely define the range over which the Fourier transform will be calculated. This approach nullifies the 2-to-the-nth-power limitation and is called a DFT.
Power Spectrum Generation Using The DFT
If it is necessary to transform a portion of the waveform with more precision than the FFT will allow, or when a non-windowed transform is desired, DFT generation is the answer. For example, if you are processing transient signals, the edges contain important information that will be unacceptably distorted by applying the window solution. In this case, you would have no choice but to use the DFT. As stated previously, the DFT allows you to adjust the end-points that define the range of the waveform to be transformed, thus eliminating the need for windowing. This approach allows a waveform containing any number of points to be evaluated, which provides more flexibility than the fixed-length, 2-to-the-nth-power FFT. However, to prevent the same leakage effect experienced with a non-windowed FFT, the DFT must be generated over a whole number of periods starting at the waveforms mean level crossing. In other words, the end-points that define the range of the waveform over which the DFT will be calculated must be adjusted to enclose or define a whole number of periods, preferably starting at or around the point where the waveform crosses its mean.
The DFT allows more versatility and precision than the FFT. However, versatility and precision come at the expense of added computation time by the algorithm and added time spent by you on end-point positioning. For example, Table 1 compares the difference in computation time required to generate an FFT and a DFT on an identical waveform using DATAQ Instruments' WWB Fourier transform utility. The times shown are in seconds and were obtained from a 386-based, 25 megahertz PC without a math coprocessor. Since the WWB Fourier transform algorithm uses integer arithmetic, a math co-processor does little to increase performance and is therefore not needed for this package. Some software packages either require a math co-processor for operation or strongly recommend one for optimal performance. Note the DFT computation times are only approximately four times slower than those of the FFT. This is because the WWB utility uses a computational technique very similar to the FFT in order to compute the DFT. The result is a much faster calculation than the standard n2 number of multiplications normally required by a DFT.
Figure 3 — The spectrum of a sine wave peaks at a single frequency as shown on top in the illustration above when an FFT is performed on a section of waveform that contains a whole number of periods. If the FFT is performed on a fractional number of periods, the spectrum gives a very different picture as shown on the bottom in the illustration above — a broad peak resulting in poorly determined frequency and inaccurate amplitude. These waveforms were generated by an inexpensive function generator, which accounts for the noise present in the spectrum.Figure 4 — The significance of window selection is well illustrated by the Fourier transformation of two sine waves close to each other in frequency, but widely differing in amplitude. (a) shows the best case, matched end-point transform where the two signal frequencies that makeup the original waveform are clearly defined, one 2.2 Hz at 90 dB and the other 10.9 Hz at 46 dB. More typically, (b) shows the transform of the same waveform only with mismatched end-points. Note that the second peak is not even visible in this spectrum. The need for a window clearly exists. The remaining transforms illustrate the degree of success attained with various windows in suppressing spectral leakage and recovering the lost frequency component. Each window was applied to the original waveform, with the result illustrating the trade-off between sharpness of peaks and decay of sidelobes. (c) shows a Hamming window. Note that this window never brings the signal to zero. (d) shows the Bartlett window, (e) shows the Hanning window, and (f) shows the Blackman window. For this spectral-separation example, the Blackman window is the best at bringing out the weaker term as a well defined peak.
What Are Waveforms
| Transform Type | Number of Points | |||||
| 512 | 1024 | 2048 | 4096 | 8192 | 16384 | |
| FFT | 0.3 | 0.6 | 0.9 | 1.4 | 2.6 | 7.3 |
| DFT | 1.3 | 2.0 | 3.3 | 5.6 | 12.6 | -- |
Table 1 — Elapsed computation time in seconds for various-point transforms. It should be mentioned that the DFT was calculated over a range of data points equal to one less than the number shown. This was done to ensure that the software would generate a DFT. If it is operating on a 2-to-the-nth-power number of data points (e.g., 1024), the software is 'smart' enough to recognize that either an FFT or a DFT can be generated from this number of data points. Since a DFT means many more unnecessary calculations, the software will take the path of least calculations, resulting in an FFT. WinDaq is capable of transforming a maximum of 16,384 data points using an FFT, and 8,191 points using a DFT.
Time Series Generation Using The IFT
As with other bilateral transformations, such as rectangular to polar coordinates, the Fourier transformation works in both directions. If the power spectrum (as a function of frequency) were to be 'run backward', the original signal would be, in principle, reconstructed as a function of time. This is known as the inverse Fourier transform (IFT). You might be questioning the purpose of an IFT if all it does is get you back to where you started. The beauty of the IFT lies in its ability to get you back to the time domain after the power spectrum has been edited in the frequency domain. This capability is very useful in power spectrum filtering applications. For example, in many cases it is desirable to examine a waveform without any 'noise' present to distort the true nature of the signal. This can be done by applying high-pass, low-pass, band-pass, and notch filter functions to the power spectrum before performing the IFT. A high-pass filter will remove all unwanted frequency components less than a designated point on the power spectrum and a low-pass filter removes all unwanted frequency components greater than the designated point. A band-pass filter is a combination of high-pass and low-pass filters applied to isolate a narrow band of interest on the power spectrum. A notch filter removes the unwanted frequency component at the designated point. Figure 5 illustrates the kind of power spectrum editing possible in the frequency domain. Filtering operations can be a powerful feature in a Fourier analysis software package.
Other Fourier Analysis Software Issues
The needs of any Fourier analysis application are best served by a graphics based software package that allows fast power spectrum editing. In addition to the basic FFT, DFT, and IFT operations, the value of a Fourier analysis software package can be further enhanced by the extra 'bells and whistles' that accompany it.
Software packages supporting waveform Fourier analysis should be capable of displaying the strength of a frequency component in either engineering units or relative magnitude (decibels) since converting power spectrum amplitude units can be a time consuming task.
Another issue is power spectrum resolution. Other than speed, resolution is the only other difference between a 512-point transform and a 16,384-point transform. A power spectrum always ranges from the dc level (0 Hz) to one-half the sample rate of the waveform being transformed, so the number of points in the transform defines the power spectrum resolution (a 512-point Fourier transform would have 256 points in its power spectrum, a 1024-point Fourier transform would have 512 points in its power spectrum, and so on). For example, if you wanted to see separate 20 and 21 Hz frequency components in the power spectrum of a complex waveform, a 512-point Fourier transform might not show these individual components clearly since its entire power spectrum is only divided into 256 equally spaced points and the desired frequencies are so close together. However, if the transform contained more points, it would be able to devote more points to the definition of closely spaced frequency components. The more the number of points in the transform, the better the frequency resolution.
(a) Original Waveform
(b) Power Spectrum of (a)
(c) Filtered Power Spectrum
(d) IFT Result
Editing takes place in the frequency domain. The waveform shown by (a) is a 20 Hz signal containing undesirable 60 Hz noise. A 512-point FFT was used to generate its power spectrum shown by (b). While in the frequency domain, all undesirable frequency components greater than the 40 Hz corner frequency (including the 60 Hz noise) were edited out, or reduced to zero by applying a low pass filter as shown by (c). An IFT was then generated from this filtered power spectrum resulting in the pure 20 Hz waveform shown by (d). Note the bell-shaped appearance of the waveform. This is due to the application of a Hanning window, a solution to the spectral leakage dilemma inherent with the FFT. Note also how the Hanning window attenuates the signal to zero at the edges of the window. Had a DFT been applied, this attenuation would be eliminated and the 20 Hz signal would be displayed at its full amplitude from end to end.
Signal Waveform Analysis
A related issue is power spectrum magnification. The software under consideration should be able to display the entire power spectrum on one screen width regardless of the number of points in the transform. This is useful for spotting the overall trend of a spectrum. With magnification, the software should also allow you to select a portion of the power spectrum plot and examine it more closely with several magnification factors. A video standard of 1024 × 768 provides 1024 picture elements (pixels) of horizontal resolution. If a 512-point Fourier transform is performed, the 256 points generated by the transform fit nicely on a screen 1024 pixels wide. The same is true of a 1024-point transform, where a 1024 pixel wide screen is more than adequate to contain the 512 points generated by the transform. The problem arises when a transform larger than 2048 points is performed. Say an 8192-point Fourier transform is performed. The 4096 points generated by the transform is much wider than the 1024 pixel width of the screen. In order to get the entire power spectrum on one screen width, a compression factor (in this case, a factor of 4) must be applied. Magnification must then be applied to examine the spectrum at the full resolution of the 8192-point transform. Additionally, when a magnification factor is applied that prohibits the display of the entire power spectrum on a single screen width, the software should allow you to pan the entire plot one screen width at a time.
Yet another feature to consider is an export facility. Is it possible to export the coordinates defining an FFT plot to an ASCII file with the software you are considering? This feature allows you to reproduce the spectrum for use in other programs.
Does the software you are considering allow you to quickly see the results of each window on the same waveform? This can be a handy and time saving feature when experimenting with the different types of windows and the results each one delivers.
Finally, the software package should be capable of power spectrum smoothing. This is best implemented by a moving average utility. A moving average is accomplished by taking two or more data points from the spectrum, adding them together, dividing their sum by the total number of data points added, replacing the first data point with the average just computed, and repeating the steps with the second, third, and so on data points until the end of the data is reached. This simple averaging technique is used to attenuate random, small amplitude frequency spikes often encountered in a power spectrum plot.
References
1Ronald N. Bracewell, 'The Fourier Transform,' Scientific American, June 1989, pp. 86-95.
This section provides you the opportunity to share your favorite diagnosis or interesting waveform capture with other AES customers.
| Tech Note 10 Testing Nissan Injection | Tech Note 5 5 Critical Sensors for Correct Engine Response |
| Tech Note 9 Using an Amp Probe with your DSO | Tech Note 4 Vacuum Waveform Case Study |
| Tech Note 8 Injector Drivers | Tech Note 3 Fuel Injector Signals |
| Tech Note 7 How to view alternator ripple (diode patterns) with the Vantage® PGM | Tech Note 2 Notes on AC Voltage |
| Tech Note 6 Coil over Plug Ignition | Tech Note 1 Solving a problem with the Vantage PGM and a Vacuum waveform |
Go to Page 2 of Tech Notes | |
We welcome you to submit your favorite experience captured with your scope or the AES Wave!
Rs232 Signal Waveform
Catch a Wave!
| Tech Note 010: Testing Nissan Ignition I often see questions over e-mail about testing Nissan distributor-power transistor ignition so I thought a post on this subject was in order. There are a lot of these on the road and for the most part it is a dependable system, but age takes its’ toll. I thought I would share with you the techniques I use to zero in on the problem along with some helpful pictures. The system is rather simple, with few components, and is best tested with the DSO. Basically, the trail starts at the distributor, which houses the optical crank angle sensor. This sensor is an LED and collector, separated by a rotating disc with many slots. The signal strategy is, as with many optical distributors, a high and low resolution signal pair. These signals are digital square wave signals. The high resolution signal has a voltage transition for each degree of crankshaft rotation, so it is called the 1 degree signal. Each voltage transition high and each low represents one degree. The low resolution signal has one signal cycle (positive and negative transition) for each cylinder. So, on a four cylinder this would be the 180 degree signal, since a cylinder is up every 180 degrees, and on a six cylinder it would be the 120 degree signal. Here is an example of a good CAS 180 and 1 degree signal relationship: And here is an example of a 120 and 1 degree relationship: The low resolution signal usually has a wider sync pulse, carbureted or fuel injected. Here is a capture of the wider sync slot in a 120 degree signal: These signals are processed by the ECU along with other inputs for spark timing. The ECU then sends out a command signal to the power transistor, which controls the coil primary. Here is a capture of the relationship between the CAS 180 degree signal and the power transistor command. Here also the wider sync pulses can be seen in the 180 degree signal. Here is an example of the relationship between the power transistor command and coil primary: And this is an example of what happens to the power transistor command when there is a 180 degree signal failure: Here is an unusual 180 degree CAS failure which became a mirror of the 1 degree signal: And here is an example of a high rpm CAS 1 degree signal failure. This one had a symptom which mimicked valve float. The symptom could only be duplicated at high rpm. The engine would begin to backfire in the intake and bog down, as the spark command failed due to the 1 degree signal failure. The power transistor command looks quite different when it is not interacting with the transistor. Here is a capture disconnected from the transistor: This is important to note since it can indicate a failed transistor. Here is an example of a failed transistor from an 8 plug Z24 pickup: Keep in mind that the eight plug systems do not trigger the exhaust side ignition under certain load and rpm ranges. That’s why secondary ignition problems on the intake side usually show up under load. So don’t be surprised to see the power transistor command disappear under these conditions. To solve ignition problems reliably on these, all you need is some basic knowledge of how the system works, some diagrams, your scope, and your instincts. Just follow the electronic trail to the source of the failure. This works in almost all cases, but you can’t win ‘em all. Case in point, an ‘88 Pathfinder with a very weak spark which would not start. Good primary pattern could be seen and the coil wire was OK. Conclusion, bad coil. WRONG. A failed power transistor was the culprit. Here’s the pic: Now when I encounter weak spark on one of these, before I condemn the coil, I try to do a primary current check as well as primary trigger. Here is an example of a Maxima no-start due to weak spark, showing good primary trigger and current: A new coil is all that was needed. I hope it has been helpful to share some of this with all of you. I welcome any additional comments or techniques. I don’t mean to make this seem simplistic. I would be the first to admit that some of these can drive you nuts. Duplicating and capturing the failure is sometimes quite difficult, and seemingly unexplainable things do occur. But I have found that this strategy works well for me in almost all cases. I have learned so much from iATN, I thought I would just try in some small way to contribute. Even though this is probably old news to most of you, I’m sure some can benefit. Tom Roberts | |
| Back to Top | |
| Tech Note 009: Using an Amp Probewith your DSO What not to Test with an Amp Probe Test These Instead The milliamp probe can be used for testing fuel pumps and other low current electric motors. Ignition coil current can also be tested with the lab scope. Most dual-channel scopes will allow you to view ignition coil current and secondary voltage at the same time with the help of an amp probe. If you have the Interro PDA allow you can view these signales by using the Scope Channel in the Engine Test menu. Figure 1 below, is a new fuel pump voltage and amperage trace on a GM CMFI system. This system requires a minimum of 8.5 amps. Figure 2 below, voltage signal is good and the amperage signal is faulty. The fuel pump motor has an open circuit and the vehicle had a loss of power. Figure 3 below, displays secondary ignition and coil current. This screen captured the crossfiring in the secondary and verified that the coil triggering was not at fault. The milliamp current probe allows quick and accurate non-intrusive testing. Test good to know good. Test good current signals and note their shapes and amplitude and then make them 'bad' and note the changes in the signal. | Related AES Links: |
| Back to Top | |
| Tech Note 008: Injector Drivers I thought I would post this short article on injector drives so those of you who are new to DSO technique and any others interested might benefit from my relatively limited knowledge and open up some feedback and discussion which would enrich all of us. Observing injector waveforms for the first time can be somewhat confusing. Hopefully this information will help to make things clearer and speed things up on the road to proper injector diagnosis. Injector drive types can be divided into three categories: Saturation (or conventional) Drivers Most of the drivers in this category are port fuel injected. This, however, is a Nissan Sentra TBI. The injector is low resistance but there is a resistor in the circuit to reduce current flow. When the ground is supplied to the injector and the driver provides the power the picture is a little different: To acquire the pattern the scope must be on the control side of the injector. Peak and Hold Driver Here is an example of a positively controlled driver: Most TBI injectors fall into this category. When measuring pulse width on these, measure from the beginning of the peak section to the end of the hold section as the cursors show. Pulse Width Modulated Below is a positively controlled pulse width modulated driver: Here, you should also measure pulse width from the beginning of the peak section to the end of the pulse modulation section as the cursors show. There are some interesting combinations too. Honda civic dual TBI units use an auxiliary injector which has a saturation driver and a main injector that uses a pulse width modulated driver. The names seem backwards though, for the auxiliary injector is used all the time and the main injector comes in under light to heavy load conditions. It is not active at idle. Here is an example of both injectors under light load cruise. The top is the Auxiliary and the bottom is the main: Here is the same under acceleration. Note the added quirk of a current limit hump in the peak section of the main driver. Common Failures Below is an example of a badly blown driver: There are varying opinions about the seriousness of this failure. I have found that the symptoms are inconsistent with the seriousness of the pattern degradation. In this example, the symptoms were serious. Unbalanced injection and resulting lean misfire with ECM compensating for resulting O2, creating a rich condition at times. Others I have seen in similar shape have no symptoms. Basically I am saying not to blame symptoms on this failure alone. What I do if I suspect that this is a possible cause is to install a .1mfd capacitor (Radio Shack # 272-1053) across the injector. This will clear up the pattern. If the symptoms are still there then move on. If that fixes it, then you just have to decide to leave it at that or replace the ECM. Often rebuilt ECM's have similarly blown drivers in them already. Shorted GM Multec injectors are common as well. The give away in the pattern is a high ground offset (the distance from the lowest point to ground), and low comparative inductive kick (spike when injector shuts off). Ground offset should be generally within 800mv of ground. The spike varies by application so compare this to others. Some ECMs will shut down when injector current gets too high to keep from going up in smoke. Below are a couple of examples of shorted injectors with saturation drivers: Below is a Quad four example of a shorted injector with a peak and hold driver. Here add to ground offset and inductive kick problems the time of the peak section before the ECM begins current limit hold section. Also note the oscillations in the hold section: And the good driver for comparison: Remember that when one shorted injector steals the current from others in parallel the good injectors may be the ones misfiring. Another common failure cannot be identified with a scope. Failed injector patterns look identical to good ones, resistance and current is the same. This failure is most common on Chrysler 2.2 and 2.5 TBI. Symptoms are stalling at idle, hesitation aggravated when cold usually. You will see the injector spray sputter or stop during the event. The engine can be made to stall lean by squeezing the return line to raise fuel pressure. A new injector clears this up. I hope this is of some help to a few of you who might be new to this DSO stuff, or maybe just don’t use it a lot. | Related AES Links: |
| Back to Top | |
| Tech Note 007: How to view alternator ripple (diode patterns) with the Vantage® PGM. The Snap-on Vantage is a great tool with many productivity enhancing features, such as, vehicle specific information and test setups. One test setup not listed on the Vantage PGM is one for testing alternators. Did you ever wonder why? Well, it is because the Vantage PGM does not have AC Input Coupling and cannot process this signal in such a way to make it useful for diagnosis. AC Input Coupling filters out the DC (steady) portion of a signal allowing only the AC (always changing) portion to appear on the display of the test equipment. Many scopes (Fluke 98, Interro PDA, OTC Perception) provide AC Input Coupling and can be used to test alternators with a set of regular test leads. But, when you combine a special type of test lead and the correct test setup with the Vantage PGM you can view the alternator ripple. The example below illustrates how to setup the Vantage PGM to test alternators with a special test lead that includes an in-line filter. The test lead picks up the signal and delivers it to the filter which blocks the DC (steady) portion of the signal and only allows the AC (always changing) portion to reach the Vantage PGM and be displayed on the screen. Combined with the correct setup on the Vantage PGM you will be able to view minor voltage changes in the AC signal without regard to the DC portion of the signal. This special test lead effectively provides AC Input Coupling for the Vantage PGM and expands its testing capabilities. Vantage Setup Brad created these graphics in MS Paintbrush, so some of the images may not look exactly the same as the display on your Vantage. Connecting the AES AC PASS FILTER
WAVEFORM SETUP SCREEN
A GOOD SAMPLE WAVEFORM A HAND DRAWN SAMPLE OF BAD WAVEFORM Note: this was not an actual waveform, but a re-creation done in paintbrush for simulation purposes.
Brad Petersen | |
| Back to Top | |
Coil over Plug Ignition The customer that owns this car drove in complaining of stalling and hard starting after the engine got hot. The engine started and ran fine stone cold. When it reached operating temperature, the engine would start misfiring. Cooling the igniter down with a choke tester restored the ignition. After replacing the igniter with an updated unit, the bad alternator diode was found. The culprit is exposed: | |
| Back to Top | |
5 Critical Sensors for Correct Engine Response
| Related AES Links: •JENDHAM handbooks and Traning Manuals |
| Back to Top | |
Vacuum Waveform Case Study The Car: The Problem: Companion cylinder is determined to be cylinder #1. #1 is repeated and verified by moving trigger clip ( ch 1 ) to cylinder #4 and going to single trace (see figure below). Static compression test is performed to test cylinder sealing and running compression test is performed to confirm cylinder breathing. Results: running (scrader pushed in)1=1002=80 3= 80 4=75 PSI Problem has been located(using the vacuum waveform) and verified using the compression testing above. Running compression should always be approx. 1/2 of cranking, compression The fact that #1 cylinder shows a higher than normal increase proves that the vacuum waveform is correct in its pattern of a different pulse on this cylinder. This vehicle needs a basic 'tune-up' and a very much needed valve adjustment. We will advise the results of the valve adjustments at a later date. Click on the image to view full size: AES Wave Note: Do the following to save images into your AES Wave! database(s): | Related AES Links: |
| Back to Top | |
Fuel Injector Signals Fuel injector signals have a few different characteristic shapes. The majority of injectors are ground controlled. Voltage is supplied to the injector when the ignition is on and the PCM controls the injector on time by completing the circuit to ground. There are a few manufacturers that turn the injector on by supplying voltage through the PCM. All of the waveforms (eight of them) presented below are typical good signals. Fig 1 Saturation Inj. Fig 2 (below) Saturation Inj. Fig 3 (below) Peak and Frequency Modulated Hold Fig 4 (below) Peak and Frequency Modulated Hold (PNP Driver) Fig 5(below) Peak and frequency modulated hold, used on some GM throttle body systems. Fig 6 (below) displays the injector current and voltage traces Fig 7 (below) displays the injector current and voltage traces Fig 8 (below) displays the injector current and voltage traces Helpful hint: Dynamic testing with a milliamp current probe is faster and more accurate than static testing with an ohmmeter. Use Ohm’s law to determine the proper current level. Adjust the values for the number of injectors that are sharing each control circuit. NOTE: Why are these waveforms distorted? I do not know, I resized them to retain Aspect Ratio and they look fine in my editing program. But when I see them online they become distorted. These were sent to me embedded in a MS Word document and that could be part of the problem. If you paste them into your AES Wave database(s) they should lose their distortion. | AES Wave Note: |
| Back to Top | |
| Tech Note 002: Notes on AC Voltage Many lab scope signals may appear to be good at a glance. However, your experience and closer examination of the trace will help to evaluate the features of a trace that will lead to the cause of the problem. Figure 1 and Fig. 2 are Ford PIP signals: Figure 1 (Above) has noise on the tops of the digital signals. The noise is caused by AC voltage riding on the DC signal. Figure 2 (below) is an example of too much AC voltage that can interrupt the signal and cause a driveability problem. When AC noise is detected on a DC signal, test the alternator ripple. With an Interro PDA, select the Diode Pattern test in the Engine Test Menu and connect the current probe to either battery cable and record the ripple pattern. With other scopes you need to enable AC input coupling. With scopes and meters without AC input coupling you should use the AES AC Pass Filter Test Lead, it filters the DC portion of the signal so you can view the AC 'always changing' portion. Figure 3 and Fig. 4 are Alternator Ripple patterns: Figure 3 (Above) Bad alternator ripple. Figure 4 (below) Good alternator ripple. | Related AES Links: |
| Back to Top Tech Note Spacer | |
| Tech Note 001: Jay Mathes A 95 Kia Sephia came in with MIL on. (Only cust complaint too.) P1523 which is a solenoid voltage below a threshold (8.5V?). The customer felt that the diagnosis of a bad battery was out of line, so I captured a simple waveform to show them (see Figure 1 below).
Static, KOEO, crank and start. It is clear that when the batt voltage drops below 7V that this code is a given. Freeze frame showed cold, 0 MPH, and monitors were all pending indicating PCM reset. The car actually started OK, although it was clear it cranked a bit slow. Another use of a Fluke PV350 (or other pres/vac transducer) is to have a cranking compression record of a vehicle. Easy to set up. I also use this set-up without the schrader valve to see the three pulses on rotary engines (see Figure 2 below).
| Jorge's Notes: Jay ----Great job! The waveform in Figure 1 and what you did with it is certainly one of the key values of a lab scope: You used it to prove your diagnosis to the customer. Showing them a DMM reading would not be the same! This one also shows the value of the record feature on the Vantage allowing you to scroll through the data to find the important events! Excellent vacuum waveform in Figure 2. This concept can go to the next step if you wish to identify the cylinder/pulses. Use a trigger pickup on plug #1 to ID the pulses. The trigger pickup will put out a spike every time #1 fires allowing you to ID and superimpose the events! Oh yes, if #1 is firing which cylinder follows with an intake stroke? Jorge Related AES Links: |