Fault diagnosis of reciprocating compressor gas valve based on local mean decomposition and Lempel-Ziv complexity

Vibration signal of reciprocating compressor gas valve presents a typical characteristics of non-stationarity and nonlinearity. An integration approach based on local mean decomposition (LMD) and Lempel-Ziv complexity (LZC) is proposed in this paper for improvement of diagnosis accuracy. First, the LMD method is applied to decompose original gas valve signals into a set of product functions (PFs), each of which is stationary signal. Then, the main PF components are selected by mutual information (MI) method with the original signal, and the LZC of each main PF is computed to form the feature vectors. Finally, the BP neural networks is applied to diagnose the faults of reciprocating compressor gas valve based on the feature vectors above. Experimental results indicate that the fault features of reciprocating compressor gas valve can be extracted by the integration of LMD and LZC. So the proposed approach is one of ways to achieve more accurate diagnosis of reciprocating compressor

Vibro-acoustic Analysis of Reciprocating Compressor in the Context of Fault Diagnosis

This project assessed the behaviour of a positive displacement type of compressor utilising airborne acoustic signatures. The study concentrated on finding an improved method based on airborne sound that can be suitable for diagnosing some common faults in reciprocating compressor (RC). Being a critical component of the industry, the condition monitoring of reciprocating compressor is very much needed to avoid any failure of its machine parts that can cause a sudden breakdown of RC. The compressor acoustic signal is a result of various mechanical forces related to the varied cylinder pressure, valve movement, turbulence air flow which in terms contribute to the periodic excitation along with the non-linearity caused by the valve fluttering, hence making the airborne signal complex and non-stationary in nature. The transient response due to the periodic impact of the valves, modulation effect due to the fluid-mechanical interaction and low signal to noise ratio (SNR) are the challenging aspects of this study. To demonstrate the vibro-acoustic property of the reciprocating compressor, first a model was developed. The leakages in valve and intercooler are very common in RC. The second most common fault which is often neglected is a clogged filter. Hence, taking into consideration, filter blockage fault is introduced for the first time in the existing test set up. Three faults (discharge valve leakage, intercooler leakage and filter blockage) are simulated, and corresponding acoustic responses are recorded for further study of the signal-nature. The model is then validated by the actual data from RC test bed. Along with the modelling of compressor acoustics, various signal processing techniques like Minimum Entropy Deconvolution (MED), Teager Energy Operator (TEO) are used on the test data to detect abnormalities present. MED in this case, is proved to be effective in finding the transient responses whereas, TEO serves as an energy detection tool for tracking the total mechanical energy. Still both methods find it difficult to come up with the best possible diagnosis results as they fail to take all the major characteristics of the RC acoustics into consideration. To overcome this challenge, higher order spectral analysis as a form of Modulation Signal Bi-Spectrum (MSB) is used to find out the most effective modulating components by enhancing the modulating characteristics and suppressing the noise. Moreover, the quadratic phase coupling allows MSB to handle the non-linearity that might be present in RC due to the valve fluttering. The proposed MSB based method not only provides a more consistent and accurate diagnosis of compressor faults but also shows that airborne acoustics has a good aspect in fault identification of RC by validating both model and test results. Recognizing that there is perpetual room for improvement, the performance of the proposed RC fault diagnosis method can be enhanced by incorporating a denoising technique developed using the Variational Mode Decomposition (VMD) associated with Kalman filtering method. The future study must also consider several other individual and compound faults that can be incorporated in the study for understanding vibro-acoustic phenomena of RC

Condition Monitoring of Gas Turbines using Acoustic Emissions

Acoustic emission (AE) technology has recently found its way in condition monitoring of rotary equipment due to its advantage of earlier detection of defects and anoma- lies in comparison to vibration analysis. However, there has been very little industrial application of AE signals for condition monitoring of safety-critical equipment if any, partly due to the diffculty in processing, interpreting and classifying the acquired data in a highly reliable fashion. The motivation in this thesis was to develop a methodol- ogy for inferring health related information in a gas turbine without intruding the engine. Our work has targeted a broad class of rotary equipment known as cyclostationary processes, therefore, instead of analyzing particular AE samples of gas turbines we have tried to build a mathematical framework that would suit any arbitrary machine comply- ing certain conditions. The result of our work mainly encompasses a feature extraction technique that eliminates the random e↵ects associated with a gas turbine AE signal, and a hypothetical testing method for classification of AE signals with any desirable level of certainty, subject to a set of assumptions and conditions. We have validated our methodologies and derivations using actual real-life gas turbine AE signals, and compared our solutions with some of the techniques published in the literature

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Bibliography of Lewis Research Center technical publications announced in 1993

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses