Vibration characterisation for fault detection and isolation in linear synchronous motor based conveyor systems

Linear synchronous motor (LSM) based transport systems are increasingly deployed in automated manufacturing environments. The aim of the study is to establish the feasibility of employing low power and low-cost vibration sensing cyber physical systems to perform near real-time fault detection and isolation for passive LSM vehicles. Empirical data capture was conducted on an LSM test-bed where vehicle velocity was varied to determine how changes in velocity would impact the vibration profile of the LSM vehicle. The recorded data was analyzed, and peak accelerations were examined for each of the velocities under study. Frequency domain analysis was conducted on the collated accelerometer data and frequencies of interest were identified. The findings are shown to concur with the manufacturer's operating specifications (0-30 Hz). A relationship between LSM vehicle speed and vibration frequency was established. The results presented provide the basis for the establishment of low-cost condition based preventative maintenance, deployed to a LSM based transport system for high volume manufacturing

On the potential for Electromagnetic Energy Harvesting for a Linear Synchronous Motor based Transport System in Factory Automation

Transport systems incorporating linear synchronous motors (LSMs) enable linear motion at high speed for emerging factory automation applications. The goal of this work is to determine the feasibility of harvesting energy directly from an operational LSM transport system employed in high volume manufacturing. Microelectromechanical (MEMs) based sensor technology, deployed as part of a wireless cyber physical system (CPS), perform near real-time magnetic field measurement for a mobile LSM vehicle. The vehicle under study is purposed for mobile factory automation and is not wired for communications nor does it have an onboard power source. A series of experiments were designed and conducted to establish the magnetic profile of the system. Empirical data capture was conducted on a cycled LSM test-bed comprising of 2 shuttles and 2 x 3 meter lengths of LSM track (MagneMotion QuickStick®100). Varying vehicle speeds were incorporated in the experimental regime to determine how changes in velocity would impact the magnetic profile of the vehicle. The recorded magnetic field data was analysed and a relationship between LSM vehicle speed and magnetic field frequency was established. The study highlights the potential to employ a single receiving coil to enable energy recovery which in turn could power a cyber-physical system (CPS) tasked with performing condition based monitoring of the LSM transport vehicles. This in turn can form the basis for the development of a predictive maintenance system, deployed to an LSM based transport layer in high volume manufacturing environments

On the use of Wireless Sensor Networks in Preventative Maintenance for Industry 4.0

The goal of this paper is to present a literature study on the use of Wireless Sensor Networks (WSNs) in Preventative Maintenance applications for Industry 4.0. Requirements for industrial applications are discussed along with a comparative of the characteristics of the existing and emerging WSN technology enablers. The design considerations inherent to WSNs becoming a tool to drive maintenance efficiencies are discussed in the context of implementations in the research literature and commercial solutions available on the market

Guidelines for deploying wireless sensor network in manufacturing: Star topology study using the RSSI to test the network performance

Wireless Sensor Networks (WSNs) are varying in kinds and can be used for many purposes. It can be extensively used in monitoring, reporting, and decision routines. Therefore, WSNs are very important to integrate with manufacturing since they can help not only in monitoring and decision making but also they can increase productivity and decrease the wiring costs. However, WSNs are facing some challenges in manufacturing as they need to resist the interferences that could be generated by the electrical and mechanical fields created during the manufacturing processes. Some of these interferences could be due to the high temperatures, blocks of metallic equipment, or even rotation of machines inside the work shop floor. In this study, a WSN network was tested in a large manufacturing company in the USA Midwest to measure the received signal strength indication (RSSI) and compared to a neutral environment. The neutral environment was selected inside a building having almost the same area as of the manufacturing area. The data were generated using the software provided by the wireless sensors kit. The RSSI is expected to decrease over distance and is expected to be less in a manufacturing environment than in neutral environment which is considered to be free of harsh interferences. The results of both the manufacturing environment and the neutral environment were collected and converted from hexadecimal values to decimal values and then statistical analysis of the data was provided. The results show that: 1. The wireless kit was able to detect differences in the RSSI value between manufacturing environment and neutral environment on distance below 90 feet. 2. When the distance becomes larger than 100 feet, the kit was not able to detect any differences between the two settings because the RSSI readings were low for both of them and the effect of the interferences was minimal. The results of this project suggest that manufacturing can adopt this technology because of the huge advantages that can be added and could be reflected directly on the processes. However, the findings also suggests that each manufacturing organization should plan, design, and test wireless sensor network application for its own processes before the implementation phase as it helps for future plans in integrating the technology in different applications. Moreover, it is important to start with simple and non-critical applications before spreading the WSN inside the manufacturing area. The implications of the WSNs improve quality and efficiency of production

The design and evaluation of Wireless Sensor Networks for applications in industrial locations

In manufacturing industries, there exist many applications where Wireless Sensor Networks (WSN\u27s) are integrated to provide wireless solution for the automated manufacturing processes. It is well known that industrial environments characterized by extreme conditions such as high temperature, pressure, and electromagnetic (EM) interference that can affect the performance of the WSN\u27s. The key solution to overcome this performance issue is by monitoring the received Signal Strength Index (RSSI) at the received sensor of the WSN device and track frame error rate of wireless packets. ZigBee is a wireless sensor network (WSN) standard designed for specific needs of the remote monitoring sensor system. Zigbee networks can be established by three different topologies: start, hybrid, and mesh. In this research project, the interest in analyzing the characteristics of the Zigbee performance was completed using a star topology network. Three performance parameters were obtained: the RSSI signal to monitor the received wireless packets from the sending node, path-lost exponent to determine the effect of industrial environment on wireless signals, and the frame error rate to know the discontinue time. The study was in three phases and took place in two settings: The first was at the manufacturing laboratory at the University of Northern Iowa, the second and the third were at the facility of a Midwestern manufacturing company. The study aimed to provide an analytical tool to evaluate the performances of Zigbee networks in industrial environments and compare the results to show that harsh environments do affect its performance. The study also involved testing the performance of WSN. This was done by simulating input/output Line passing with digital and analog data. Packets were sent from one node and counted at the receiving side to measure the packet error rate of WSN in industrial environment. In conclusion, investigating the WSN\u27s systems performance in industrial environment provides is crucial to identify the effects of the harsh conditions. It is necessary to run similar investigation to prevent the malfunction of the manufacturing applications. Testing a simple WSN in industrial environment can be capable of predicting the performance of the network. It is also recommended to have an embedded approach to WSN applications that can self-monitor its performance

Distributed fault detection in industrial system based on sensor wireless network

10.1016/j.csi.2008.03.024Computer Standards and Interfaces313573-578CSTI

Optimisation of vibration monitoring nodes in wireless sensor networks

This PhD research focuses on developing a wireless vibration condition monitoring (CM) node which allows an optimal implementation of advanced signal processing algorithms. Obviously, such a node should meet additional yet practical requirements including high robustness and low investments in achieving predictive maintenance. There are a number of wireless protocols which can be utilised to establish a wireless sensor network (WSN). Protocols like WiFi HaLow, Bluetooth low energy (BLE), ZigBee and Thread are more suitable for long-term non-critical CM battery powered nodes as they provide inherent merits like low cost, self-organising network, and low power consumption. WirelessHART and ISA100.11a provide more reliable and robust performance but their solutions are usually more expensive, thus they are more suitable for strict industrial control applications. Distributed computation can utilise the limited bandwidth of wireless network and battery life of sensor nodes more wisely. Hence it is becoming increasingly popular in wireless CM with the fast development of electronics and wireless technologies in recent years. Therefore, distributed computation is the primary focus of this research in order to develop an advanced sensor node for realising wireless networks which allow high-performance CM at minimal network traffic and economic cost. On this basis, a ZigBee-based vibration monitoring node is designed for the evaluation of embedding signal processing algorithms. A state-of-the-art Cortex-M4F processor is employed as the core processor on the wireless sensor node, which has been optimised for implementing complex signal processing algorithms at low power consumption. Meanwhile, an envelope analysis is focused on as the main intelligent technique embedded on the node due to the envelope analysis being the most effective and general method to characterise impulsive and modulating signatures. Such signatures can commonly be found on faulty signals generated by key machinery components, such as bearings, gears, turbines, and valves. Through a preliminary optimisation in implementing envelope analysis based on fast Fourier transform (FFT), an envelope spectrum of 2048 points is successfully achieved on a processor with a memory usage of 32 kB. Experimental results show that the simulated bearing faults can be clearly identified from the calculated envelope spectrum. Meanwhile, the data throughput requirement is reduced by more than 95% in comparison with the raw data transmission. To optimise the performance of the vibration monitoring node, three main techniques have been developed and validated: 1) A new data processing scheme is developed by combining three subsequent processing techniques: down-sampling, data frame overlapping and cascading. On this basis, a frequency resolution of 0.61 Hz in the envelope spectrum is achieved on the same processor. 2) The optimal band-pass filter for envelope analysis is selected by a scheme, in which the complicated fast kurtogram is implemented on the host computer for selecting optimal band-pass filter and real-time envelope analysis on the wireless sensor for extracting bearing fault features. Moreover, a frequency band of 16 kHz is analysed, which allows features to be extracted in a wide frequency band, covering a wide category of industrial applications. 3) Two new analysis methods: short-time RMS and spectral correlation algorithms are proposed for bearing fault diagnosis. They can significantly reduce the CPU usage, being over two times less and consequently much lower power consumptio