Liqun Hou

System requirements for industrial wireless sensor networks

The employment of device monitoring, diagnosis and condition-based maintenance is one of the potential approaches for enhancing operational efficiency and reducing energy consumption of industrial machines. Industrial wireless sensor networks (IWSNs) for device monitoring are appealing to industry due to their inherent advantages compared with traditional wired systems, such as low cost, convenience of installation and re-location. In this paper, firstly the design requirements of IWSNs are outlined based on surveying application examples and commercial systems. Secondly the wireless protocol standards, current off-the-shelf wireless sensor platforms and prototypes developed by individual researchers for IWSNs are listed and compared. The paper then describes two techniques for improving the efficiency and effectiveness of industrial wireless sensors systems - on-sensor data processing, and a modified MAC protocol for improved real-time performance. Finally possible solutions to deal with particular requirement of IWSNs, including a system architecture and a novel protocol stack for a monitoring system are discussed.

Induction motor fault diagnosis using industrial wireless sensor networks and Dempster-Shafer classifier fusion

This paper presents a novel induction motor fault diagnosis system using industrial wireless sensor networks (IWSNs), in which on-sensor feature extraction and fault diagnosis approaches are investigated to address the tension between the higher system requirements of IWSNs and the resource constrained characteristics of sensor nodes. Classifier fusion using Dempster-Shafer theory on the coordinator is then explored to increase diagnosis result quality. Three kinds of motor operating condition - normal, loose feet, and mass imbalance - are monitored to evaluate the proposed system. Experimental results show on-sensor feature extraction and fault diagnosis could effectively reduce payload transmission data, and decrease node energy consumption, while Dempster-Shafer classifier fusion significantly improves fault diagnosis accuracy compared with using local neural network classifiers alone.

Induction motor condition monitoring using industrial wireless sensor networks

An induction motor condition monitoring system using industrial wireless sensor networks (IWSNs) with and without on-sensor data processing are described and compared. The prototype system has been implemented and validated on a single phase induction motor in the laboratory. On-sensor data processing is explored to alleviate the contradictions between the higher performance requirements of IWSNs such as higher sample rate and fast transmission rate, and the resource constrained characteristic of IWSNs nodes, such as the limited data rate and battery energy. The experimental results and motor stator current waveforms on different working conditions are given. The test, comparison and analysis about data transmission rate, energy consumption and node lifetime for on-sensor data processing and direct raw data transmission are also presented.

Wireless underwater power and data transfer

This work investigates whether a contactless, wireless underwater coupling could be developed for underwater sensor networks. This requires the wireless transmission of power from the sensor hub to the transducer module, and the two-way wireless data communication between hub and transducer. Results from a trial deployment of systems with conventional waterproof couplings show that these are a major shortcoming of existing systems. Experiments are conducted which demonstrate that a Zigbee transceiver, operating in the 2.4GHz band, can communicate with low error rates up to 40mm at low RF power (-25dBm) and up to 70mm at higher power (-3 dBm) in seawater. Ranges are slightly higher in fresh water. Inductive power transfer, using a split transformer design, can transmit low power, in the 50-100mW range with efficiency of approximately 50%, demonstrating that wireless sensor couplings are feasible.

A novel industrial wireless sensor network for condition monitoring and fault diagnosis of electrical machines

A novel industrial wireless sensor network (IWSN) for condition monitoring and fault diagnosis of electrical machines is presented, in which on-sensor fault diagnosis based on principal component analysis is explored to address the tension between the high system requirements of electrical machine monitoring and the resource constrained characteristics of IWSN sensor nodes. The prototype system is evaluated with a single phase induction motor monitoring system. Normal motor working conditions and two types of motor faults, ie. loose feet and mass imbalance, are monitored to validate the feasibility of the proposed system. The results show that using on-sensor fault diagnosis can reduce transmission data by 99.8%, decrease energy consumption, and prolong node lifetime from 106 to 153 h, an increase of 44%. The experimental results also indicate that the proposed approach has high fault diagnosis accuracy.

Energy Efficient Machine Condition Monitoring Using Wireless Sensor Networks

An Industrial Wireless Sensor Network system is described for condition monitoring of electric machines. On-sensor data processing is used to reduce the amount of information that needs to be transmitted, thus saving communications energy. Based on a condition monitoring interval of 3 seconds, and using 2 AAA batteries for power, the system lifetime is measured for four different operating modes. For raw data transmission to the coordinator, the system lifetime is 106 hours. If feature extraction is done on the sensor node, this is extended to 152 hours. If fault diagnosis is done on the node, the lifetime is 153 hours. If fault diagnosis is conducted every 3 seconds but results are only sent under fault conditions, or once per hour as a health check, then lifetime is dramatically increased to 1764 hours.