Abstract
Wireless sensor networks have received increasing research attention and they can be found in every field of life. The industrial wireless sensor network is one of the boosting and emerging technologies for machine fault diagnosis and monitoring. This study provides a review on vibration fault diagnosis approaches in industrial wireless applications and discusses the causes of machine faults and challenges. Several advanced vibration approaches have been used to quantify machine operating conditions. These approaches provide a fault diagnosis mechanism and expert maintenance solutions through analysis of vibration. The review also shows a broad scope of research for developing a robust fault diagnosis approaches in the field of industrial wireless sensor networks.
Highlights
Modern industries are composed with different devices and equipment to improve the process efficiencies and meet financial objectives
The industrial wireless sensor network applications provide a range of monitoring and controlling features to machinery
Artificial intelligence approaches are more feasible for fault diagnosis and for complex machines
Summary
Modern industries are composed with different devices and equipment to improve the process efficiencies and meet financial objectives. We review and compare the artificial intelligence based fault diagnosis approaches in industrial wireless applications. Industrial wireless sensor networks target the low cost and smart applications, with feasible data throughput for fault diagnosis and monitoring in industries. Another disadvantage of wireless technologies the data analysis carries out to extract a set of is half duplex operation of transceivers, where uncorrelated features and detect the fault modes in order transceivers are not able to transmit and receive by same to solve the problem. Distortion and significant noise of transceiver and circuitry created by strong motors, electrical discharge devices These issues lead to data loss and delay issues in industries
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