Bearing Fault Detection For Water Pumping System Using Artificial Neural Network

Dutta, Nabanita; Palanisamy, K.; Subramaniam, Umashankar

doi:10.1109/icpc2t53885.2022.9776994

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Wang et al [15] focus on linear timevarying systems, employing zonotopic unknown input observers for robust fault detection. Dutta et al [16] utilize machine learning for bearing fault detection in motors. Gong and Qiao [17] focus on wind turbine fault detection using synchronous sampling in stator currents.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Discriminating Faults and Replay Attacks in Hybrid Systems

Badr,

Talebi,

Khosravi

2024

IEEE Access

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This study proposes an innovative approach specifically devised for discriminating faults and replay attacks in the context of hybrid system, significantly advancing cyber-physical system security. At the core of our method is the intricate process of embedding distinct series of non-recurring alphanumeric codes in each cell of the output matrix. This advanced encoding strategy is essential for the accurate identification of replay attacks, as it allows for the detailed analysis of code pattern variations that are indicative of malicious activities. Our approach is uniquely effective in revealing subtle anomalies and replicated data patterns, marking a substantial enhancement in the detection of sophisticated cyber threats. Complementing this is the implementation of a Sliding Mode Observer for precise fault detection, enabling our system to simultaneously differentiate between faults and replay attacks. This dual detection strategy is thoroughly demonstrated in complex system scenarios, highlighting its effectiveness as a robust defense mechanism. The introduction of this innovative method represents a significant step forward in fortifying the security and integrity of modern hybrid systems. A four-tank hybrid system is considered as a case study, to show the effectiveness of the proposed approach in identifying and countering faults and replay attacks within the system.INDEX TERMS adaptive threshold, cyber-physical systems, fault detection, hybrid system, replay attack.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach for Discriminating Faults and Replay Attacks in Hybrid Systems

Badr,

Talebi,

Khosravi

2024

IEEE Access

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“…In literature [16], the influence of high-amplitude blade pass frequency (BPF) vibration on rotor fault detection is analyzed. Literatures [17][18][19][20][21] use single variable analysis or method migration for state identification and fault diagnosis. The purpose of literature [17] is to examine whether Motor current signature analysis (MCSA) can be used to detect faults in a wet-rotor pump.…”

Section: Introductionmentioning

confidence: 99%

“…In literature [20], a Failure Modes and Effects Analysis (FMEA) is used to analyze typical motor control designs with the intent of determining a means of preventing a runaway condition due to failure of any one component. Literature [21] develops the Matlab Simulink model for the identification of bearing fault in induction motor-based pumping systems. Literatures [22][23][24][25][26][27] use the improved machine learning algorithm to identify and diagnose the states of pump loads.…”

Section: Introductionmentioning

confidence: 99%

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

Liu

Zhu

et al. 2023

Electronics

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Main circulation pump is the only high-speed rotating equipment in primary loop of nuclear power plant. Its function is to ensure the normal operation of primary loop system by controlling the circulating flow of reactor coolant. In order to ensure long-term healthy operation of nuclear power main circulating pump, a method for identifying the health states of nuclear power main circulating pump based on ensemble empirical mode decomposition (EEMD) and support vector machine optimized by optimized quantum genetic algorithm (OQGA-SVM) is proposed. Vibration signal of main circulating pump is decomposed by EEMD. Vibration signal characteristics of nuclear power main circulating pump in healthy state and different fault states are analyzed and target characteristic indexes are put forward. Then, health state identification model of main circulation pump of OQGA-SVM is established, and target characteristic indexes are used as input parameter of the model. Finally, combined with experimental data, the model analysis and validation show that the health state identification method of nuclear power main circulating pump based on EEMD-OQGA-SVM can accurately and effectively identify the states of main circulation pump, has a higher identification accuracy than EEMD-SVM method and is more efficient and accurate than EEMD-QGA-SVM method.

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A novel fault diagnosis scheme for a ballast system

Hui-fang

Zhang

2023

Automatica

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Bearing Fault Detection For Water Pumping System Using Artificial Neural Network

Cited by 5 publications

References 14 publications

A Novel Approach for Discriminating Faults and Replay Attacks in Hybrid Systems

A Novel Approach for Discriminating Faults and Replay Attacks in Hybrid Systems

Health State Identification Method of Nuclear Power Main Circulating Pump Based on EEMD and OQGA-SVM

A novel fault diagnosis scheme for a ballast system

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