Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis

2012 ◽  
Vol 61 (10) ◽  
pp. 2787-2798 ◽  
Author(s):  
Liqun Hou ◽  
Neil W. Bergmann
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wireless Sensor ◽  
Machine Condition ◽  
Industrial Wireless Sensor Networks ◽  
Machine Condition Monitoring

scholarly journals Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4113 ◽  
Author(s):  
Xiaoli Tang ◽  
Xianghong Wang ◽  
Robert Cattley ◽  
Fengshou Gu ◽  
Andrew Ball
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Condition Monitoring ◽  
Wireless Sensor ◽  
Energy Sources ◽  
Field Energy ◽  
Machine Condition ◽  
Machine Condition Monitoring ◽  
Harvesting Systems

Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made.

scholarly journals Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

2021 ◽  
Vol 10 (1) ◽  
pp. 245-254
Author(s):  
Xiaoran Zhang ◽  
Kantilal Pitambar Rane ◽  
Ismail Kakaravada ◽  
Mohammad Shabaz
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Internet Of Things ◽  
Machine Learning Algorithms ◽  
Vibration Monitoring ◽  
Wireless Sensor ◽  
Industrial Wireless Sensor Networks ◽  
Industrial Automation ◽  
Internet Of Things Technology

Abstract Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.

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