Energy Efficient Machine Condition Monitoring Using Wireless Sensor Networks

Bergmann, Neil W.; Huang, Liqun

doi:10.1109/wcsn.2014.65

Cited by 9 publications

(12 citation statements)

References 16 publications

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“…The general architecture used for the fault diagnosis system uses wireless sensor network which has one base station and a n number of sensor nodes as shown in Figure 1. The radio present in the sensor node uses IEEE 802.15.4 and ZigBee protocols for transmission of vibration [13]. Fig.…”

Section: Methodsmentioning

confidence: 99%

“…To illustrate the process, bearing faults like inner race fault, outer race fault and inner and outer race fault were created and the corresponding vibration signatures were taken. The footstep in [13] is followed to process the acquired vibration signals.…”

Section: Methodsmentioning

confidence: 99%

“…In WSN, a typical battery capacity might be 10.8 kJ at 3V [13]. The power consumed is about 25 to 100 mW during transmission or reception.…”

Section: Introductionmentioning

confidence: 99%

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Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

2018

Teh. vjesn.

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The breakdown in a machine created by a fault will greatly affect the plant operation. The frame work of fault diagnosis of machines using machine learning techniques is an established area. Here, the fault diagnosis system is implemented with the help of wireless sensor networks (WSN). Each machine in the plant is fitted with sensors (node) from which the fault diagnosis is carried out. The signals/messages from each node are transmitted to a base station, which acts as a central control unit for the entire plant. Fault diagnosis using WSN in a factory setup has few challenges. The major issue in WSN is the life time of the nodes, as they are situated far away from base station in many plants like thermal power plant, refinery, and petroleum industries. To address the life time of the nodes many researchers have developed many protocols like LEACH, SEP, ERP, HCR, HEED, and PEGASIS. The plants need customisation in terms of choosing suitable algorithms and choosing location of the base station within the plant for better life time of the nodes. This paper presents results of both experimental and simulation studies of a typical plant, where the vibration signals from each machine are acquired and through machine learning techniques the fault diagnosis is performed with the help of wireless sensor networks. For illustrative purpose, a well reported bearing fault diagnosis data set is taken up and fault diagnosis case study was performed from wireless sensor networks point of view (experimental study). Here, at every stage, the computational time is taken as a primary concern which affects the life time of the sensor nodes. Then, the WSN of 18 sensor nodes representing 18 machines with LEACH protocol is simulated in Matlab© to study the life time characteristics of each node while keeping base station at different locations. The life time of different nodes is heavily dependent on the location of the base station. Finding the right location of the base station for a given plant is another contribution of this work.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

2018

Teh. vjesn.

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“…1. A fault diagnosis system of a wind turbine using WSN uses IEEE 802.15.4 and ZigBee protocols to transmit the signals from wind turbine to base station [9]. The basic structure consists of a sensor node equipped in every wind turbine located in the field and one base station is kept for a certain group of wind turbines.…”

Section: Simulation Studymentioning

confidence: 99%

Fault diagnosis of wind turbine bearing using wireless sensor networks

Ramalingam¹,

Annamalai²,

Vaithiyanathan³

2017

THERM SCI

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This paper proposes wireless sensor networks to monitor the condition of wind turbines. It addresses lifetime maximization issue of sensor nodes using stable election protocol for a cluster of upto nine wind turbines. This paper presents results of both experimental and simulation studies of a wind turbine plant, in which the vibration signals from each wind turbine are taken and with the help of machine learning technique, the fault diagnosis is done for a plant with wireless sensor networks. An experimental case study is performed from a wireless sensor networks with a well reported wind turbine bearing fault diagnosis data set. The outcome of the study shows that if the number of wind turbines is five for one base station, then the lifetime of the sensor nodes are maximum using MATLAB.

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“…Currently, the emerging of Internet of Things (IoT) and its deployment in industrial settings, namely Industrial Internet of Things (IIoT), are transforming traditional industries in many areas including machine fault diagnosis [2][3][4][5][6]. IIoT and its wireless implementation, industrial wireless sensor networks (IWSNs), can sense device information and then transmit this data via a base station and the Internet to powerful cloud servers to enable real-time wireless machine condition monitoring and fault diagnosis [7,8].…”

Section: Introductionmentioning

confidence: 99%

IWSNs with On-sensor Data Processing for Machine Fault Diagnosis

Hou¹,

Hao²,

Ma³

et al. 2018

Preprint

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Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.

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Energy Efficient Machine Condition Monitoring Using Wireless Sensor Networks

Cited by 9 publications

References 16 publications

Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

Lifetime Maximization of Wireless Sensor Networks for a Fault Diagnosis System Using LEACH Protocol

Fault diagnosis of wind turbine bearing using wireless sensor networks

IWSNs with On-sensor Data Processing for Machine Fault Diagnosis

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