With the rapid development of the Internet of Things (IoT), industrial big data can now be collected through many different sources, such as multimedia. Intelligent fault diagnosis is recognized as an important and promising approach in using these data, because it can provide accurate diagnosis and adjust to different deployed environments. In this study, intelligent fault diagnosis approaches for real machine data sets are comprehensively investigated. First, support vector machine (SVM) and popular neural network approaches are implemented and compared. Results show that while the neural network-based method is very efficient in many high-dimensional applications, such as video, SVM performs well enough for intelligent fault diagnosis. Second, the relation between the number of samples and the efficiency of diagnosis are studied, Findings indicate that a small number of samples can produce an optimal result. Furthermore, accuracy does not increase with training data because of the inherent fuzziness of machine monitoring data. Finally, the accuracy of conditioning and diagnosis is demonstrated for several kinds of machine data.
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This work is supported by National Key R&D Program of China (2017YFB0404201).
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Si, J., Li, Y. & Ma, S. Intelligent Fault Diagnosis for Industrial Big Data. J Sign Process Syst 90, 1221–1233 (2018). https://doi.org/10.1007/s11265-017-1316-9
- Big data
- Fault diagnosis
- Internet of things
- Neural network