Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

Altaf, Saud; Soomro, Muhammad Waseem; Mehmood, M. S.

doi:10.1155/2017/1292190

Cited by 15 publications

(7 citation statements)

References 16 publications

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“…A separate type of measurement locations was supposed during certain positions to monitor the behavior of each electrical machine in Bearing-related faults account for the vast majority of failure modes (52.5%). When the fault with the bearings and the faults with the stator are considered together, they account for more than 87.5% of the overall faults that are already present [16]. However, faults that are related to bearings are typically caused by mechanical and vibrational issues [17].…”

Section: Distributed Motor Network Modelmentioning

confidence: 99%

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

et al. 2022

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The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

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Section: Distributed Motor Network Modelmentioning

confidence: 99%

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

et al. 2022

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“…There are more studies on isolated machine fault diagnosis [1][2][3][4][5] than multiple motors' signal fault diagnosis [6,7]. Raw data acquired from sensors were preprocessed before being used for further analysis.…”

Section: Signal Processing Techniquesmentioning

confidence: 99%

Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network

Altaf¹,

Ahmad²

2020

Modeling and Simulation in Engineering - Selected Problems

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The machinery arrangements in industrial environment normally consist of motors of diverse sizes and specifications that are provided power and connected with common power-bus. The power-line could be act as a good source for travelling the signal through power-line network and this can be leave a faulty symptom while inspection of motors. This influence on other neighbouring motors with noisy signal that may present some type of fault condition in healthy motors. Further intricacy arises when this type of signal is propagated on power-line network by motors at different slip speeds, power rating and many faulty motors within the network. This sort of convolution and diversification of signals from multiple motors makes it challenging to measure and accurately relate to a certain motor or specific fault. This chapter presents a critical literature review analysis on machinefault diagnosis and its related topics. The review covers a wide range of recent literature in this problem domain. A significant related research development and contribution of different areas regarding fault diagnosis and traceability within power-line networks will be discussed in detail throughout this chapter.

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“…The literature is filled with potential FDM systems [4]. Unfortunately, the majority of the FDM systems proposed by researchers make use of supervised learning models [5], [6], [7] and they suffer from certain flaws. In the case of supervised systems, manual data collection is required.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection System for Stepper Motors

Sharrar¹

2022

IJERECE

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Predictive maintenance (PdM) systems have the potential to autonomously detect underlying motor issues at early stages. Although many such systems have been proposed up to data, they have yet to be implemented. Most of these methods, which are based on supervised learning, require hours of manual data collection and annotation. Furthermore, they are mostly made to tackle a single instead of the multiple motor issues that may occur and are unable to adapt to varying motor speed and load conditions. Thus, they are not viable for industrial implementation. Therefore, this paper presents an unsupervised LSTM autoencoder-based anomaly detection system for electric motors. It analyzes the vibration and current consumption data from motors to detect anomalies, which is sufficient to account for the various motor defects. The system comes with a variety of features that allows users to autonomously collect data, train models and deploy models. In addition to that, users can remotely keep track of the motor’s conditions. To test the system, a hardware test bench using a stepper motor is made to simulate defective conditions. The LSTM Autoencoder-based anomaly detection system is described step-by-step in this paper.

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Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

Cited by 15 publications

References 16 publications

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network

Anomaly Detection System for Stepper Motors

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