An Intelligent Diagnostic Method for Wear Depth of Sliding Bearings Based on MGCNN

Dai, Jingzhou; Tian, Ling; Chang, Haotian

doi:10.3390/machines12040266

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…In recent years, PAM has become vital in asset-intensive industries, driven by data digitization for long-term operational excellence [6]. The emphasis on data-driven decision making in Industry 4.0 has significantly broadened the application of PAM, particularly in maintenance, where costs can represent 15% to 40% of total operational costs and their reduction can be significant, providing compelling reasons for companies to implement PAM [9,10].…”

Section: Physical Asset Managementmentioning

confidence: 99%

Advancing Predictive Maintenance with PHM-ML Modeling: Optimal Covariate Weight Estimation and State Band Definition under Multi-Condition Scenarios

Godoy,

Mavrakis,

Mena

et al. 2024

Machines

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The proportional hazards model (PHM) is a vital statistical procedure for condition-based maintenance that integrates age and covariates monitoring to estimate asset health and predict failure risks. However, when dealing with multi-covariate scenarios, the PHM faces interpretability challenges when it lacks coherent criteria for defining each covariate’s influence degree on the hazard rate. Hence, we proposed a comprehensive machine learning (ML) formulation with Interior Point Optimizer and gradient boosting to maximize and converge the logarithmic likelihood for estimating covariate weights, and a K-means and Gaussian mixture model (GMM) for condition state bands. Using real industrial data, this paper evaluates both clustering techniques to determine their suitability regarding reliability, remaining useful life, and asset intervention decision rules. By developing models differing in the selected covariates, the results show that although K-means and GMM produce comparable policies, GMM stands out for its robustness in cluster definition and intuitive interpretation in generating the state bands. Ultimately, as the evaluated models suggest similar policies, the novel PHM-ML demonstrates the robustness of its covariate weight estimation process, thereby strengthening the guidance for predictive maintenance decisions.

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Section: Physical Asset Managementmentioning

confidence: 99%

Advancing Predictive Maintenance with PHM-ML Modeling: Optimal Covariate Weight Estimation and State Band Definition under Multi-Condition Scenarios

Godoy,

Mavrakis,

Mena

et al. 2024

Machines

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Neuro-Fuzzy Framework for Fault Prediction in Electrical Machines via Vibration Analysis

Kudelina,

Raja

2024

Energies

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The advent of Industry 4.0 has ushered in a new era of technological advancements, particularly in integrating information technology with physical devices. This convergence has given rise to smart devices and the Internet of Things (IoT), revolutionizing industrial processes. However, despite the push towards predictive maintenance, there still is a significant gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on bearing faults. This study compares the effectiveness of traditional neural network algorithms with a novel approach integrating fuzzy logic. Through extensive experimentation and analysis of vibration spectra from various mechanical faults in bearings, it is demonstrated that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.40% compared to 94.34%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection. While initial results are encouraging, further validation with more complex fault scenarios and additional fuzzy layers is recommended to fully explore the potential of fuzzy-neuro networks in fault prediction for electrical machines.

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An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM

Lin,

2024

Sensors

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Combining medical IoT and artificial intelligence technology is an effective approach to achieve the intelligence of medical equipment. This integration can address issues such as low image quality caused by fluctuations in power quality and potential equipment damage, and this study proposes a predictive model, ISSA-TCN-BiLSTM, based on a bi-directional long short-term memory network (BiLSTM). Firstly, power quality data and other data from MRI and CT equipment within a 6-month period are collected using current fingerprint technology. The key factors affecting the active power of medical equipment are explored using the Pearson coefficient method. Subsequently, a Temporal Convolutional Network (TCN) is employed to conduct multi-layer convolution operations on the input temporal feature sequences, enabling the learning of global temporal feature information while minimizing the interference of redundant data. Additionally, bidirectional long short-term memory (BiLSTM) is integrated to model the intermediate active power features, facilitating accurate prediction of medical equipment power quality. Additionally, an improved Sparrow Search Algorithm (ISSA) is utilized for hyperparameter optimization of the TCN-BiLSTM model, enabling optimization of the active power of different medical equipment. Experimental results demonstrate that the ISSA-TCN-BiLSTM model outperforms other comparative models in terms of RMSE, MSE, and R2, with values of 0.1143, 0.1157, 0.0873, 0.0817, 0.95, and 0.96, respectively, for MRI and CT equipment. This model exhibits both prediction speed and accuracy in power prediction for medical equipment, providing valuable guidance for equipment maintenance and diagnostic efficiency enhancement.

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An Intelligent Diagnostic Method for Wear Depth of Sliding Bearings Based on MGCNN

Cited by 3 publications

References 49 publications

Advancing Predictive Maintenance with PHM-ML Modeling: Optimal Covariate Weight Estimation and State Band Definition under Multi-Condition Scenarios

Advancing Predictive Maintenance with PHM-ML Modeling: Optimal Covariate Weight Estimation and State Band Definition under Multi-Condition Scenarios

Neuro-Fuzzy Framework for Fault Prediction in Electrical Machines via Vibration Analysis

An Efficient Prediction Model on the Operation Quality of Medical Equipment Based on Improved Sparrow Search Algorithm-Temporal Convolutional Network-BiLSTM

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