Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

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Nowadays, the ever increasing need for higher accuracy, reliability and security in modern industries has given rise intensively to the use of multi-sensor data fusion method in fault diagnosis of industrial equipment. In this article, an effective and powerful method for precise fault diagnosis of planetary gearbox based on fusion of vibration and acoustic data using the Dempster–Shafer theory is presented. For this purpose, the vibration and acoustic signals in different modes of the gears were first received simultaneously by two separate sensors and then were transmitted from time domain to time–frequency domain using wavelet analysis. After signal processing, each sensor's data were transferred to a local classifier for primary fault diagnosis. Local classification was performed by artificial neural network classifier. The outputs of the local classification were used as the inputs into Dempster–Shafer rules for fusion of classifiers and achieving the final accuracy of the classification. In primary fault diagnosis, the accuracy of fault classification based on vibration and acoustic signals was obtained as 86% and 88%, respectively. After incorporating the outcomes of two sensors, the final accuracy of the classification was calculated as 98% which indicates a 10% jump compared to single-sensor mode. These results indicate the effectiveness of the data fusion method in condition monitoring and fault diagnosis of the equipment. Moreover, in this article, the capability of Dempster–Shafer theory in the fusion of uncertain data and the increase of accuracy in the classification was demonstrated to a quiet acceptable level.

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In this article, an intelligent system based on an artificial neural networks (ANN) classifier is proposed for fault diagnosis and classification of planetary gearboxes based on fusing acoustic and vibration data at the feature level. First, the acoustic and vibration signals of the planetary gearbox were collected simultaneously in four gearbox conditions: (1) healthy; (2) worn tooth on planet gear; (3) cracked tooth on ring gear; and (4) broken tooth on ring gear. Then, the time domain signals were transformed to the time-frequency domain by wavelet transform. Thirty statistical features were then extracted from each signal and used as feature vectors to an ANN classifier. The primary classification of the faults was undertaken based on the extracted features from each sensor. The classification accuracy of acoustic and vibration data was about 88.4% and 86.9%, respectively. The final classification accuracy using fused features was 98.6%, indicating the superiority of the proposed method for fault diagnosis of a planetary gearbox. The 10% accuracy increase gained through using the data fusion method can significantly enhance the quality and accuracy of fault diagnosis and, as a result, condition monitoring of the machinery.