Fault Diagnosis of Reciprocating Compressors Using Revelance Vector Machines with A Genetic Algorithm Based on Vibration Data

This paper focuses on the development of an advanced fault classifier for monitoring reciprocating compressors (RC) based on vibration signals. Many feature parameters can be used for fault diagnosis, here the classifier is developed based on a relevance vector machine (RVM) which is optimized with genetic algorithms (GA) so determining a more effective subset of the parameters. Both a one-against-one scheme based RVM and a multiclass multi-kernel relevance vector machine (mRVM) have been evaluated to identify a more effective method for implementing the multiclass fault classification for the compressor. The accuracy of both techniques is discussed correspondingly to determine an optimal fault classifier which can correlate with the physical mechanisms underlying the features. The results show that the models perform well, the classification accuracy rate being up to 97% for both algorithms

Testing based on the RELAY model of error detection

RELAY, a model for error detection, defines revealing conditions that guarantee that a fault originates an error during execution and that the error transfers through computations and data flow until it is revealed. This model of error detection provides a fault-based criterion for test data selection. The model is applied by choosing a fault classification, instantiating the conditions for the classes of faults, and applying them to the program being tested. Such an application guarantees the detection of errors caused by any fault of the chosen classes. As a formal mode of error detection, RELAY provides the basis for an automated testing tool. This paper presents the concepts behind RELAY, describes why it is better than other fault-based testing criteria, and discusses how RELAY could be used as the foundation for a testing system

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Motor current signal analysis using a modified bispectrum for machine fault diagnosis

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment