Characterising Vibro-Acoustic Signals of a Reciprocating Compressor for Condition Monitoring

Machine monitoring in industries such as chemical process plants, petroleum refineries and pulp and paper industries has significantly increased over the years, mainly because of the economic impact associated with the breakdown of a piece of equipment. With downtime sometimes costing up to 100,000 USD a day (Wachel, N.D), industrial organisations have made it mandatory to put in place systems for monitoring the condition of critical machines used for production purposes to prevent unforeseen machine breakdown. Reciprocating compressors are one of the widely used compressor types in diverse fields of application particularly in the oil and gas industry or chemical industry. In these industries, reciprocating compressors are mainly used to deliver high-pressure gas from one location to another. Due to the importance of these machines in delivering high-pressured air and sometimes toxic gases safely, their reliability has gained widespread interest over the years.

To improve reciprocating compressor operational performance and reliability, this research focuses on investigating the characteristics of vibro-acoustic signals from a reciprocating compressor based on a comprehensive analysis of non-intrusive vibration measurement and discharge gas oscillations (pulsations). This study will provide more knowledge on using two techniques (vibration and gas pulsations) for online monitoring and diagnosing of reciprocating compressor faults. Other monitoring techniques such as in-cylinder pressure, instantaneous angular speed (IAS), airborne acoustic as well as vibration are previously reported in literature, however, it is believed that no information for condition monitoring of discharge gas pulsation of a reciprocating compressor has been explored.

To fulfil this study, in-depth modelling and an extensive experimental evaluation for different and combined faults common to reciprocating compressor systems are explored for a wide discharge pressure range to better understand the vibro-acoustic sources. Three common faults including discharge valve leakage, intercooler leakage, discharge pipeline leakage and two combined faults: discharge valve leakage and intercooler leakage, discharge valve leakage and discharge pipeline leakage under various discharge pressures are studied in this thesis. The simulation of compressor performance with and without faults for several discharge pressures were in good agreements with the corresponding experimental evaluations, and was used to understand the compressor dynamics. Furthermore, a preliminary study on the effectiveness of conventional methods such as time-domain and frequency-domain analysis of both vibration and gas pulsation measurements were investigated. Results show that, these traditional methods were insufficient in revealing fault characteristics in the vibration signal due to the usual noise contamination and nonstationary nature of the signal. Although, with the gas pulsation signal, waveform patterns and resonant frequencies varied with faults at several discharge pressures, nevertheless, effective band pass filtering needed to identify the best frequency band that can represent the characteristic behaviour of gas pulsation signals proofed difficult and time consuming.

Amongst several advanced signal-processing approaches reviewed such as wavelet transform, time synchronous average, Hilbert transform, and empirical mode decomposition; wavelet packet transform is regarded as the most powerful tool to describe gas pulsation and vibration fault signals in different frequency bands. A combination of wavelet packet transform (WPT) and Hilbert transform (envelope analysis) is proposed to achieve optimal and effective band pass filtering for resonance band identification in gas pulsation signals, and WPTs de-noising property, which can effectively reduce excessive noise revealing key transient features in vibration signals.

Optimal band selection for vibration signal was achieved using entropy computation. The band with the highest entropy was used to reconstruct the signal and the envelope of the new vibration signal was used for classification. The fundamental frequency and its harmonics were used as a tool for fault classification. All fault conditions were clearly separated using the fundamental frequency and its third (3X) harmonic.

Regarding gas pulsation signals, the optimal band was selected by computing the root mean square (RMS) values of all eight enveloped band signals for several discharge pressures and faults. The band with the best RMS separation trend was selected for further classification using two main diagnostic features: the kurtosis and entropy of optimal band. The plot of kurtosis against entropy as a diagnostic tool showed good valve fault classification across a wide discharge pressure range.

Although the analysis of vibration signal using the proposed methods gave more reliable results for reciprocating compressor fault detection and diagnosis compared to the gas pulsation results, analysis of gas pulsation signals gave a better result on the optimal frequency band selection that can represent the behaviour of reciprocating compressor (RC) valve fault. Therefore, it can be deduced that analysis of the RC vibration signal together with the gas pulsation signal has a promising potential to be used for condition monitoring and fault diagnostics of reciprocating compressors online.