On the Impact of Local Processing for Motor Monitoring Systems in Industrial Environments Using Wireless Sensor Networks

Abstract

This paper presents a theoretical study for verifying the impact of using smart nodes in motor monitoring systems in industrial environments employing Wireless Sensor Networks (WSNs). Structured cabling and sensor deployment are usually more expensive than the cost of the sensors themselves. Besides the high cost, the wired approach offers little flexibility, making the network deployment and maintenance a complex process. In this context, wireless networks present a number of advantages compared to wired networks as, for example, the ease and speed of deployment and maintenance and the associated low cost. However, WSNs have several limitations, such as the low bandwidth and unreliability, especially in harsh environments (e.g., industrial plants). This paper presents a theoretical study on the performance of WSNs for motor monitoring applications in industrial environments, taking into account WSNs' characteristics (i.e., unreliability and communication and processing latency). The results obtained through mathematical models were analyzed together with experimental results, and it was demonstrated that employing intelligent nodes with local processing capabilities is essential for the applications under consideration, because it reduces the amount of data transmitted over the network allowing monitoring even in scenarios with high interference rate, paying off the extra latency resulting from local processing.

Application of Wireless Sensor Network Technology in Security Control of Intelligent Buildings

“…To address these problems, researchers proposed signal classification and energy detection algorithms such as the maximum-minimum eigenvalue method, artificial neural network algorithm and SVM method, but these methods all have shortcomings like poor detection effect, low sensing accuracy [17][18]; regarding the low WSN-based multi-task monitoring and scheduling efficiency and poor fault tolerance, etc., researchers proposed minimum time algorithm, genetic algorithm and minimum execution time/earliest finish time algorithm, etc. [19][20][21], but as the multi-task coordination inside buildings is a problem integrating data and computational task, the above methods can easily result in local node overload, low search efficiency, and low task assignment success rate in the calculation process [22][23][24][25].…”

Contributions to Industrial Wireless Sensor Networks

“…In industry, sensors are deployed to monitor critical parameters such as vibration, temperature, pressure and motor efficiency [Delgado Gomes et al 2013]. The measurements obtained by them are transmitted wirelessly to a sink node, which provides the information for analysis by a monitoring central, or to be used in control systems.…”