# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
## 4 Measurement and Analysis
Their corresponding frequency values are not identifiable on the spectrogram, but are easily identified on the cepstrum. By clicking the \"Acc\", \"Vel\", and \"Disp\" QRadioButtons in the \"Functions\" QGroupBox, the corresponding real-time signal waveform and its spectra are displayed, as well as the historical peak values are read from the database and displayed in \"Historical Peak\" plot. Similarly, the time-domain waveforms and spectra of the overall, low, and high frequency bands of the vibration acceleration/velocity/displacement signals of each sensor are switched by the QRadioButtons in the \"Frequency Band\" QGroupBox. The QTabWidget control is used to paginate the display. Each sensor occupies one page. In the \"Channel selection\" QGroupBox of the measurement settings interface, as shown in Figure 5, the user can enable/disable data acquisition of any channel (i.e., sensor). Accordingly, the statistics and graphical interface of that channel in Figure 3 is added/removed. Figure 4: The side bands in simulated gearbox vibration signals by using cepstrum analysis. The threshold values are set in relation to the monitored device, i.e., the threshold values are different for different monitored devices. These thresholds may be given by experienced technicians and adjusted through the measurement settings interface shown in Figure 5. When the amplitude of the vibration signal is gradually increased, a regular warning or a severe alarm is issued depending on how much the threshold value is exceeded, as shown in the bottom right of Figure 3. A log of the alarm messages is printed, including the timestamp, channel, signal type, signal value, and other information of the generated warning/alarm. Finally, we deployed the monitoring system we developed to vibration monitoring of water pumps in a water company. At the test site shown in Figure 6, there are seven large-scale pumps, as well as gearboxes and pipes. The vibration acceleration sensor is attached to the pump/motor housing with a strong magnet. The Raspberry Pi embedded system is connected to the DAU via a 5G industrial router. We verified the accuracy of our monitoring system by comparing the measurement results with those of the AR63A, a dedicated instrument used for field testing by the water company. The measurement results show that the performance of our monitoring system is consistent with those of the AR63A. To verify whether the vibration analysis platform works properly over time, the stability of the system needs to be tested. No faults were detected in the tests because the device was operating normally and could not be intentionally damaged to create faults. However, for the noise brought by the external power supply of the DAU, the power harmonic can be for seen from the spectrum. Moreover, the collected vibration signal is usually coupled with heavy noises, such as stemming from sensor imperfections, poor running environment, or background noise, and so on. Besides, the vibration signals are normally non-stationary and nonlinear. What is worse, is that the vibration signal may be too weak to be distinguished from the noise. Therefore, it is still a challenge to extract vibration signals for fault diagnosis under high noise levels and strong harmonic interference. Several of the above experimental measurements show that the proposed fault prognosis algorithm meets the needs of the water company. We are also well aware that more advanced deep learning-based algorithms, such as those proposed in [26; 27], use non-contact vibration images for fault diagnosis and work well under strong noise. The designed analyzer uploads the raw vibration data that the proposed fault prognosis algorithm has assessed to be faulty to a remote server. This provides a sufficient source of data for subsequent deep learning-based algorithms. Figure 5: Measurement settings interface.