Conclusions

This research has investigated, developed and validated the signal processing methods used for analysing the non-stationary signals for machinery condition monitoring and

fault diagnosis. Specially, the research has focused on the condition monitoring and fault diagnosis of a two-stage reciprocating compressor and a diesel engine. The research work was carried out through analysis of typical non-stationary signals measured from these two test rigs and a variety of signal processing methods were employed. Conclusions arising from the work carried out are summarised in the following sections.

9.2.1 Conclusions regarding the fault diagnostics of the compressor

using the motor electrical current signal

Conclusion 1: The electromagnetic force and corresponding nonlinear current signal in the stator are produced as a result of the fault in the rotor system and its downstream equipment. Analysis of the electromagnetic relationship to the electrical current signal in Section 4.3 showed that the motor current signal could be employed widely for condition monitoring of the induction machine and its downstream equipment. Fault information of various types can be obtained through analysis of the sideband components of the current signal.

Conclusion 2: Analysis of the characteristics of the electrical current signal, as given in Section 4.3, is a reliable and effective method for fault diagnosis of the compressor. Combination of this with the analysis of phase properties of dynamic time warping method (see Section 3.5), demonstrates that phase compensation based dynamic time warping (see Section 4.4) is an effective method for processing the electrical current signal for accurate feature extraction and compressor fault diagnosis.

Conclusion 3: The analysis of electrical current signals based on the proposed dynamic time warping method has significant potential for the detection and identification of the presence of incipient faults in the two-stage compressor (see Section 4.5). The analysis results indicate that the dynamic time warping method can be employed to extract accurate features of the current signals. The RMS values of the residual signals can also be used to differentiate between faulty and healthy signals and to identify the difference between other fault types under various loads and discharge pressures.

Conclusion 4: Compressor fault detection and classification based on the modified dynamic time warping methods were discussed in Section 4.5 and overcame the limitation of the Fourier transform based analysis method. From this work, it can be concluded that the accuracy and reliability of detection and classification using dynamic time warping based analysis is higher than that from Fourier transform based spectrum and envelope analysis (see Section 4.5.3). In addition, the dynamic time warping processing procedure based entirely on the time domain analysis is easier to apply in real-time monitoring processes.

9.2.2 Conclusions regarding the condition monitoring of diesel

engines

Conclusion 5: In this research work, vibro-acoustic signals were adopted as the main indicator of engine condition. Experimental analysis of results confirmed that the vibro-acoustic based analysis was effective and useful for the condition monitoring of engines. The vibro-acoustic based analysis has potential advantages for implementation in online condition monitoring systems.

Conclusion 6: Analysis of engine noise and vibration characteristics in the time domain and spectrum analysis (see Section 5.3) shows that the variation of the engine vibration and noise depends on the engine operating speed and load, and also on the variation of the combustion cylinder pressure. Therefore, it can be concluded that engine vibration and noise variation has the potential for use as an indicator of combustion process instead of using in-cylinder pressure.

Conclusion 7: The analysis of fuel monitoring based on engine noise in Section 5.5 demonstrated that the effects of room acoustics on engine acoustic signals (see Section 5.4) influences the analysis of acoustic signals in the low frequency domain below 1 kHz. A number of resonant frequencies and harmonics are introduced which cause difficulties in feature extraction from the measured engine acoustic signals. Therefore, room acoustic effects need to be suppressed and signal to noise ratios enhanced in order to obtain accurate detection results for monitoring the operating conditions of the

engine. Analysis results shows that engine operating conditions can be monitored through analysing the engine acoustic signals in the higher frequency band.

Conclusion 8: Engine vibration and acoustic signals are typical non-stationary signals which contain a large number of short time transient events (see Section 5.3). Based on the signal processing analysis of the non-stationary components of the engine vibration and acoustic signals (see Section 7.5 and 8.4) it has been shown that useful features can be extracted from the engine vibration and acoustic signals for engine condition monitoring and fuel evaluation.

Conclusion 9: The analysis of engine vibro-acoustic signals using coherent power spectrum analysis, Wigner-Ville distribution (WVD) (see Section 7.3) and fractional Fourier transform (FRFT) (see Section 7.4) methods in chapter 7 demonstrated that combustion induced noise is located in the frequency range from 2 kHz to 3 kHz (see Section 7.4.3), and can be used to identify fuel quality and for fuel evaluation of the diesel engine (see Section 7.5).

Conclusion 10: Analysis of engine acoustic signals using time synchronous average (TSA) (see Section 8.3) and continuous wavelet transform (see Section 8.2) techniques in chapter 8 has shown that peak cylinder pressure is higher when the engine is operated using rapeseed oil biodiesel rather than diesel. The RMS values of the continuous wavelet transform coefficients of the engine acoustic signal corresponded to the variation of the in-cylinder pressure and indicated the variation of engine operating conditions (see Section 8.4). Therefore, it is possible, using the continuous wavelet transform coefficients analysis technique in the higher frequency range, to evaluate fuel combustion characteristics and engine condition monitoring.