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.

In this study, an intelligent method was implemented for the detection and classification of chickens by infected Clostridium perfringens type A based on their vocalization. To this aim, the birds were first divided into two groups that were placed in separate cages with 15 chickens each. Chickens were inoculated with Clostridium perfringens type A on day 14. In order to ensure the absence of secondary diseases and their probable effect on bird vocalization, vaccines for common diseases were administered. During 30 days of the experiment, chicken vocalization was recorded every morning at 8 a.m. using a microphone and a data collection card under equal and controlled conditions. Sound signals were investigated in time domains, and 23 features were selected. Using Fisher Discriminate Analysis (FDA), five of the most important and effective features were chosen. Neural Network Pattern Recognition (NNPR) structure with one hidden layer was applied to detect signals and classifying healthy and unhealthy chickens. Firstly, this neural network was trained with 34 samples, after which eight samples were tested for accuracy. Classification accuracy was 66.6 and 100% for days 16 and 22; i.e., two and eight days after the disease, respectively. The results of this study demonstrated the usefulness and effectiveness of intelligent methods for diagnosing diseases in chickens.

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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.

This article deals with fault detection of an alternator based on vibration signals using wavelet transform and least square support vector machine. Firstly, the noise in the vibration signal is removed using wavelet denoising. The denoised signals are then analysed using discrete wavelet transform with Daubechies mother wavelet. Several statistical features are then extracted from discrete wavelet transform coefficients of the signals. Finally, least square support vector machine is employed to detect and classify the different alternator conditions. The results show that the detection accuracy reached 90.48%. Hence, the proposed procedure is capable of detecting the alternator faults, and thus can be used for practical applications.