Advances in Machine Condition Monitoring and Fault Diagnosis

Yang, Wenxian; Zimroz, Radosław; Papaelias, Mayorkinos

doi:10.3390/electronics11101563

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…The non-linear and non-stationary signals can be converted to linear and stationary signals in the form of IMF using EMD. The main disadvantage of using EMD is its mode mixing problem, which is defined as the intermittence and aliasing in the time frequency distribution [26,27]. A new method Ensemble EMD has been developed using intermixing the white noise with the signal to eliminate the mode mixing problem.…”

Section: Signal Processing Of Raw Vibration Datamentioning

confidence: 99%

Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning

Guleria,

Kumar,

Singh

2024

Eng. Res. Express

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This study presents an approach that combines variational mode decomposition (VMD) and relevance vector machine (RVM) as a prediction technique for surface roughness during turning. The method helps machinists detect irregularities during machining, such as looseness in machine parts or tool wear. The study uses work hardened EN8 steel and extracts vibration signal features into five modes. PSO optimized RVM prediction models are developed using these modes with and without combining cutting parameters. The most sensitive mode is selected for accurate surface roughness prediction. The results show that the first mode of decomposition when combined with cutting parameters provides the least mean square error of 0.1095. Surface roughness was found to be primarily influenced inversly by cutting speed and directly by feed rate respectively. The signal with high surface roughness value has high amplitude noise scattered over the large frequency range. The first decomposed mode of vibration signal increases noise with a large amplitude as surface roughness increases. The approach can be integrated into a microcontroller to regulate machine settings based on vibration data.

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Section: Signal Processing Of Raw Vibration Datamentioning

confidence: 99%

Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning

Guleria,

Kumar,

Singh

2024

Eng. Res. Express

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“…The objective of FM is to detect issues promptly, enabling operators to carry out repairs and maintenance work proactively, before the system being monitored sustains damage or experiences downtime. Furthermore, its objective extends to provide more accurate information for the optimization of machine operation [ 4 ]. Analysis of device logs and system performance metrics, running diagnostic tests, and event correlation are examples of methods employed in FM operations.…”

Section: Introductionmentioning

confidence: 99%

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Bofill,

Abisado,

Villaverde

et al. 2023

Sensors

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High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision making.

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“…In high-voltage motor equipment, we closely attend to the running state of the motor, which is determined by the harsh working environment and high-precision structure of the equipment. 1,2 The general construction diagram of a highvoltage permanent magnet motor is shown in Figure 1. * During equipment operation, magnet failure will lead to low operation efficiency or even damage to the entire motor.…”

Section: Introductionmentioning

confidence: 99%

Magnetic tile fault detection of high‐voltage electric machines: A multiview feature selection method via consistent soft label

Chen,

Yang,

Liu

et al. 2023

Circuit Theory & Apps

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Image‐based scheme has attracted wide attention in the fault detection of high‐voltage permanent magnet motors, but it often suffers from the shooting conditions. Multiview feature selection allows multisource information to be fused, which can improve the accuracy and robustness of image detection. Therefore, we propose multiview unsupervised consistency via soft‐label feature selection (MUCSFS). This method constructs consistent pseudo labels through soft labels from clustering affinity of each view sample and builds the model by integrating selection constraints into the graph model. This model filters the fault data set to obtain the feature subset, which is used for clustering. We verify the method's effectiveness by simulating multiview data. The fault clustering experiment on the high‐voltage motors' magnetic tile fault data set confirm that our method can effectively cluster the fault categories.

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Advances in Machine Condition Monitoring and Fault Diagnosis

Cited by 9 publications

References 13 publications

Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning

Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Magnetic tile fault detection of high‐voltage electric machines: A multiview feature selection method via consistent soft label

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