Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Sharma, Snehsheel; Tiwari, Shalabh K.

doi:10.1177/10775463231178070

Cited by 3 publications

(7 citation statements)

References 42 publications

(41 reference statements)

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“…Ten seconds were given for collecting data for three different health conditions: worn, chipped, and normal teeth. Sharma et al [29] used an electromechanical system (EMS) with a parallel gearbox (PGB) acting as a load machine on an induction motor (IM) in their experiment. Several faults-chipped sun gear, pitted planet gear, and combination faults-were induced on the gears using an end milling machine.…”

Section: Discussionmentioning

confidence: 99%

“…A planetary gearbox is a type of gearbox in which the output and input have the same axis of rotation. Koutsoupakis et al [2], Dabrowski et al [114], Sharma et al [29], and Lipinski et al [140] collected vibration data from the planetary gearbox.…”

Section: Planetary Gearboxmentioning

confidence: 99%

“…Predictive maintenance helps prevent the occurrence of these breakdowns by predicting possible problems before they become catastrophic failures. These machines are equipped with sensors [25][26][27][28][29] that continuously collect data on parameters like temperature [9,20,25,30], pressure [7,20] and vibration [21]. These data are then evaluated by software, which uses cutting-edge methods such as machine learning [31], previous data, and contextual information to construct predictive models that identify expected issues or maintenance requirements based on usage patterns and environmental factors.…”

Section: Introductionmentioning

confidence: 99%

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An In-Depth Study of Vibration Sensors for Condition Monitoring

Hassan,

Panduru,

Walsh

2024

Sensors

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Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.

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Section: Discussionmentioning

confidence: 99%

Section: Planetary Gearboxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An In-Depth Study of Vibration Sensors for Condition Monitoring

Hassan,

Panduru,

Walsh

2024

Sensors

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“…Machine learning and deep learning algorithms provide efficient methods for fault identification in vibration signals. In machine learning, features are usually extracted [13,28,31,36] from the signals and then machine learning classifiers like Random Forests [8,[37][38][39], Support Vector Machines (SVMs) [1,31,40,41] are applied. These classifiers are trained using extracted features, to differentiate between healthy and faulty signals.…”

Section: Introductionmentioning

confidence: 99%

Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery

Hassan,

Panduru,

Walsh

2024

Data

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Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns and maintaining operational safety. In this review, we look at different methods of vibration data processing in the context of vibration-based condition monitoring for heavy machinery. We divided primary approaches related to vibration data processing into three categories–signal processing methods, preprocessing-based techniques and artificial intelligence-based methods. We highlight the importance of these methods in improving the reliability and effectiveness of heavy machinery condition monitoring systems, highlighting the importance of precise and automated fault detection systems. To improve machinery performance and operational efficiency, this review aims to provide information on current developments and future directions in vibration-based condition monitoring by addressing issues like imbalanced data and integrating cutting-edge techniques like anomaly detection algorithms.

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“…Furthermore, the intelligent diagnosis of three-phase motors using multi-sensor data fusion has garnered significant research attention. In addition to vibration signals, current and other signals can effectively characterize fault characteristics [17]. Scholars have focused on integrating multi-sensor data from different sources into a unified framework.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Variable-Condition Motors Using a Dual-Mode Fusion Attention Residual

Xie

Wang

et al. 2023

JMSE

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Electric motors play a crucial role in ship systems. Detecting potential issues with electric motors is a critical aspect of ship fault diagnosis. Fault diagnosis in motors is often challenging due to limited and noisy vibration signals. Existing deep learning methods struggle to extract the underlying correlation between samples while being susceptible to noise interference during the feature extraction process. To overcome these issues, this study proposes an intelligent bimodal fusion attention residual model. Firstly, the vibration signal to be encoded undergoes demodulation and is divided into high and low frequencies using the IEEMD (Improved Ensemble Empirical Mode Decomposition) composed of the EEMD (Ensemble Empirical Mode Decomposition) and the MASM (the Mean of the Standardized Accumulated Modes). Subsequently, the high-frequency component is effectively denoised using the wavelet packet threshold method. Secondly, current data and vibration signals are transformed into two-dimensional images using the Gramian Angular Summation Field (GASF) and aggregated into a bimodal Gramian Angle Field diagram. Finally, the proposed model incorporates the Self-Attention Squeeze-and-Excitation Networks (SE) mechanism with the Swish activation function and utilizes the ResNeXt architecture with a Dropout layer to identify and diagnose faults in the multi-mode fusion dataset of motors under various working conditions. Based on the experimental results, a comprehensive discussion and analysis were conducted to evaluate the performance of the proposed intelligent bimodal fusion attention residual model. The results demonstrated that, in comparison to traditional methods and other deep learning models, the proposed model effectively utilized multimodal data, thereby enhancing the accuracy and robustness of fault diagnosis. The introduction of attention mechanisms and residual learning enable the model to focus more effectively on crucial modal data and learn the correlations between modalities, thus improving the overall performance of fault diagnosis.

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Residual signal–based condition monitoring of planetary gearbox using electrical signature analysis

Cited by 3 publications

References 42 publications

An In-Depth Study of Vibration Sensors for Condition Monitoring

An In-Depth Study of Vibration Sensors for Condition Monitoring

Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery

Intelligent Fault Diagnosis of Variable-Condition Motors Using a Dual-Mode Fusion Attention Residual

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