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Fault diagnosis system for industrial machinery based on embedded HTML5

Regarding state-of-the-art fault diagnosis system for industrial machinery, specific handheld embedded device is a common solution. The complexity and high cost limit the development of fault diagnosis in industry area. To develop a cost-effective and ease-of-use maintenance technology, this paper presents a novel research work using wireless tablet and embedded HTML5 for machine fault diagnosis. The small foot-print embedded dongle acts as a web server using embedded HTML5 WebSocket to improve the performance of real-time communication. Meanwhile, the dongle also severs as an access point (AP) to the mobile device. The diagnosis algorithm based on complete ensemble empirical mode decomposition (CEEMD), singular value decomposition (SVD) and support vector machine (SVM) is presented and evaluated. This paper also presents an application of the proposed technique to real-world current data obtained from a dedicated fault diagnosis experimental test bed via Siemens SINAMICS drive system. The experiment results show that embedded HTML5 has significant advantages in industrial machinery fault diagnosis, and algorithm can meet the requirement of fault diagnosis in industry field.