Getting source characterization first-time right, can be a challenging task. To achieve good quality, it is essential to detect overloads, detached sensors, broken cables, and noise issues.
When performing source characterization, you are interested in modeling the primary excitation, which is the vibration generated by the active component. Any additional excitation not produced by the active component (e.g., noise, test-bench machinery) is a secondary excitation.
When you characterize an active component, you will always model both excitations. Secondary excitations are, of course, unwanted, but always present. So, you must check how much of the secondary excitation is modeled in your calculation and how to reduce it, if needed. To do so, you perform noise measurements (where the primary excitation is switched off) and compare them to the operational measurements.
We recommend performing noise measurements before the operational measurements and compare them with all the operational conditions you want to study. Noise measurements are needed to gain insight in the quality of the Blocked Forces and their validation. You also need to calculate Blocked Forces of the secondary excitations (Blocked Force noise) and compare them with Blocked Forces of the operational measurement.
To perform noise measurements, you need to record secondary excitations at different stages, from the most silent condition (everything switched off) to the noisiest one and then the active component in operation. At each noise stage measurement, you “add” noise and perform a measurement (of about 10 seconds).
For example, when testing an electric motor, we usually measure these stages:
To evaluate the signal quality, you have to compare the operationals to the noise measurements. You must check the signal at all sensor locations, evaluating both indicator and validation/target sensors.
General rules:
If you have a good signal-to-noise of your raw data, you are mainly modeling the primary excitation and the contribution of the secondary ones is limited. When this is the case, you can go on and perform additional operational measurements and post-process them.
If you have a bad signal to noise, you are modeling both primary and secondary excitations. In this case, the secondary excitations are too high compared to the primary, and decrease the quality of your model. According to the issue, you can take different actions to improve the quality of the model.
If your primary excitation source is too low, you can:
If your secondary excitation is too high, you must understand whether it is caused by vibrational or electromagnetic noise.
Vibrational noise is any vibration generated by the other components (e.g., tire noise when analyzing the engine) or by the auxiliary machinery (e.g., dyno, pumps, etc.). When they are too high, you can:
Electromagnetic noise is any noise generated by sensors, cables and so on. When this noise is too high, you can:
When you are satisfied with the signal quality of your measurements, you can proceed and start the post-processing. The first step consists of calculating the Blocked Forces of both the operational and the noise measurements and plotting them together for comparison.
The Blocked Forces of the noise measurements must be far lower than the forces measured during operational excitation. If not, the Blocked Forces of the primary excitation are polluted with secondary excitations. In general, all the guidelines provided above for raw data also apply to Blocked Forces.
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