A parametric study on the dynamic response of reciprocating compressor with a revolute clearance joints

Due to the friction and manufacturing and assembly errors, the clearance of revolution joints is unavoidable. When the clearance value increases to a certain value, the influence of clearance on the performance of the equipment could not be ignored. In this paper, the contact collision model is used to describe the effect of clearance. The model of reciprocating compressor is simplified properly, and the influence of revolution joint clearance on the system is studied by contact impact function. The influence of parameters on the acceleration of piston is studied by changing the size, position of clearance and velocity of crankshaft. The Lyapunov exponent of crosshead acceleration under the condition of load and no load are compared, which lays the foundation for studying the vibration of reciprocating compressor caused by clearance

Applications of machine learning to reciprocating compressor fault diagnosis: a review

Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed

The impact of cracks on the performance of photovoltaic modules

This paper presents a statistical approach for identifying the significant impact of cracks on the output power performance of photovoltaic (PV) modules. Since there are a few statistical analysis of data for investigating the impact of cracks in PV modules in real-time long-term data measurements. Therefore, this paper will demonstrate a statistical approach which uses two statistical techniques: T-test and F-test. Electroluminescence (EL) method is used to scan possible cracks in the examined PV modules. Moreover, virtual instrumentation (VI) LabVIEW software is used to predict the theoretical output power performance of the examined PV modules based on the analysis of I-V and P-V curves. The statistical analysis approach has been validated using 45 polycrystalline PV modules at the University of Huddersfield, UK

Condition monitoring and fault detection of inverter-fed rotating machinery

Condition monitoring of rotating machinery is crucial in industry. It can prevent long term outages that can prove costly, prevent injury to machine operators, and lower product quality. Induction motors, often described as the workhorse of industry, are popular in industry because of their robustness, efficiency and the need for low maintenance. They are, however, prone to faults when used improperly or under strenuous conditions. Gearboxes are also an important component in industry, used to transmit motion and force by means of successively engaging teeth. They too are prone to damage and can disrupt industrial processes if failure is unplanned for. Reciprocating compressors are widely used in the petroleum and the petrochemical industry. Their complex structure, and operation under poor conditions makes them prone to faults, making condition monitoring necessary to prevent accidents, and for maintenance decision-making and cost minimization. Various techniques have been extensively investigated and found to be reliable tools for the identification of faults in these machines. This thesis, however, sets out to establish a single non-invasive tool that can be used to identify the faults on all these machines. Literature on condition monitoring of induction motors, gearboxes, and reciprocating compressors is extensively reviewed. The time, frequency, and time-frequency domain techniques that are used in this thesis are also discussed. Statistical indicators were used in the time domain, the Fourier Transform in the frequency domain, and Wavelet Transforms in the time-frequency domain. Vibration and current, which are two of the most popular parameters for fault detection, were considered. The test rig equipment that is used to carry to the experiments, which comprised a modified Machine Fault Simulator -Magnum (MFS-MG), is presented and discussed. The fault detection strategies rely on the presence of a fault signature. The test rig that was used allows for the simulation of individual or multiple concurrent faults to the test machinery. The experiments were carried out under steady-state and transient conditions with the faults in the machines isolated, and then with multiple faults implemented concurrently. The results of the fault detection strategies are analysed, and conclusions are drawn based on the performances of these tools in the detection of the faults in the machinery

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

Condition Monitoring and Fault Diagnosis of Fluid Machines in Process Industries

Condition Monitoring (CM) of fluid machines plays a critical role in maintaining efficient productivity in many processing industries. Conventional vibration techniques generally provide more localised information with the need for many sensors, associated data acquiring and processing efforts, which are difficult for system deployment and are reluctantly accepted by those industries, for example paper mills and food production lines making marginal profits. To find adequate CM techniques for such industries this research investigates a new cost- effective scheme of implementing CM, which combines the high diagnostic capability of using Surface Vibration (SV) with the global detection capability of using the Instantaneous Angular Speed (IAS) measurements and Airborne Sound (AS). To address specific techniques involved in the scheme, this research is arranged in three consecutive Phases: Phase I is the technical evaluation; Phase II is the field implementation practices and Phase III is the application of AS through Convolution Neural Networks (CNN). In Phase I, widely used reciprocating compressor is investigated numerically and experimentally, which clarifies the performances of SV, IAS, AS, pressure and motor current in a quantitative way for differentiating common faults such as leakages happening in valves and intercoolers, faulty motor drives and mechanical transmission systems. It paves the foundations for the field implementation in Phase II. In Phase II, this novel scheme is realised on three sets of vacuum pumps in a paper mill. Based on an analytic study of dynamic responses to common faults on these pumps, a field test was conducted to verify the feasibility of the scheme and the preliminary study shows that airborne sound can show the relative spectral components for each machine to a good degree of accuracy. Knowledge gained from the preceding phases of study is now applied to Phase III. New techniques based on airborne signal differences through CNN have been demonstrated to give a good indication of the sound propagation and location of noise sources under all operating discharge pressure conditions at 100% validation accuracy, proving that the state of the art deep leaning approaches can be used to deal with complicated acoustic data