Application of a new EWT-based denoising technique in bearing fault diagnosis

Fault diagnosis of tractor auxiliary gearbox using vibration analysis and random forest classifier

In this paper, a new hybrid intelligent technique is presented based on the improvement in the feature selection method for multi-fault classification. The bearing conditions used in this study include healthy condition, defective inner ring, defective outer ring, and the faulty rolling element at different rotating motor speeds. To form the feature matrix, at first, the vibration signals are decomposed using empirical mode decomposition and wavelet packet decomposition. Then, the time and frequency domain features are extracted from the raw signals and the components are obtained from the signal decomposition. The high-dimensional feature matrix leads to increasing the computational complexity and reducing the efficiency in the classification accuracy of faults. Therefore, in the first stage of the feature selection process, the redundant and unnecessary features are eliminated by the FDAF-score feature selection method and the preselected feature set is formed. The FDAF-score technique is a combination of both F-score and Fisher discriminate analysis (FDA) algorithms. Since there may exist the features that are not susceptible to the presence of faults, the binary particle swarm optimization (BPSO) algorithm and the support vector machine (SVM) are used to select the optimal features from the preselected features. The BPSO algorithm is used to determine the optimal feature set and SVM classifier parameters so that the predictive error of the bearing conditions and the number of selected features are minimized. The results obtained in this paper demonstrate that the selected features are able to differentiate the different bearing conditions at various speeds. Comparing the results of this article with other fault detection methods indicates the ability of the proposed method.

A new intelligent fault diagnosis method for bearing in different speeds based on the FDAF-score algorithm, binary particle swarm optimization, and support vector machine

In the process of performing technological operations, any agricultural machinery is exposed to a large number of changing factors. The result of performing technological operations, i.e. the assessment of performance indicators, in most cases, is performed by the organoleptic method, which is characterized by a significant error (up to 40%) and labour intensity. The use of automatic means of control gives an undoubted advantage in convenience of use and in speed of obtaining results. Given the complexity and labour intensity of assessing the quality indicators of agricultural machinery, even partial automation of this process in practice gives a significant (tens of percent) economic effect and becomes the basis for the formation of digital agriculture. We have analyzed the existing methods for controlling the quality indicators of technological operations using the example of the grain harvesting process. In particular, the possibilities of improving the methods of monitoring the quality indicators of the work of the combine harvester are considered.

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Investigation of the reasons for the failures of the tractor microclimate system

Maintenance scheduling is a fundamental element in industry, where excessive downtime can lead to considerable economic losses. Active monitoring systems of various components are ever more used, and rolling bearings can be identified as one of the primary causes of failure on production lines. Vibration signals extracted from bearings are affected by noise, which can make their nature unclear and the extraction and classification of features difficult. In recent years, the use of the discrete wavelet transform for denoising has been increasing, but studies in the literature that optimise all the parameters used in this process are lacking. In the current article, the authors present an algorithm to optimise the parameters required for denoising based on the discrete wavelet transform and thresholding. One-hundred sixty different configurations of the mother wavelet, threshold evaluation method, and threshold function are compared on the Case Western Reserve University database to obtain the best combination for bearing damage identification with an iterative method and are evaluated with tradeoff and kurtosis. The analysis results show that the best combination of parameters for denoising is dmey, rigrSURE, and the hard threshold. The signals were then distributed in a 2D plane for classification through an algorithm based on principal component analysis, which uses a preselection of features extracted in the time domain.

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Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects

Fault diagnosis based on pattern recognition approach has three main steps viz. feature extraction, sensitive features selection, and classification. The vibration signals acquired from the system under study are processed for feature extraction using different signal processing methods. Followed by feature selection process, classification is performed. The challenge is to find good features that discriminate the different fault conditions of the system, and increase the classification accuracy. This paper proposes the use of Pareto method for optimal feature subset selection from the pool of features. To demonstrate the efficiency and effectiveness of the proposed fault diagnosis scheme, numerical analyses have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter gearbox in healthy and faulty conditions. First, features are extracted from vibration signals in time, spectral, and time-scale domain, then ranked according to three different criterions namely: Fisher score, correlation, and Signal to Noise Ratio (SNR). Afterword, data formed by only the selected features is used as input for the classification problem. The classification task is achieved using Support Vector Machines (SVM) method. The proposed fault diagnosis scheme has shown promising results. Using only the feature subset selected by Pareto method with Fisher criterion, SVMs achieved 100% correct classification.

Semcheddine Fedala

Papers

Feature Selection Scheme Based on Pareto Method for Gearbox Fault Diagnosis