Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Sepulveda, Natalia Espinoza; Sinha, Jyoti

doi:10.3390/machines8040066

Cited by 23 publications

(27 citation statements)

References 27 publications

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“…Step-1 while Table 3 contains the overall results in Step-2. The results also confirm that the optimised parameters earlier suggested [14] are useful for the 2-Steps approach. These parameters should be used for any rotating machines.…”

Section: Contains the Overall Results Insupporting

confidence: 73%

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Robust vibration-based faults diagnosis machine learning model for rotating machines to enhance plant reliability

Espinoza-Sepulveda¹,

Sinha²

2021

Maint. reliab, cond. monit.

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Plant availability and reliability can be improved through a robust condition monitoring and fault diagnosis model to predict the current status (healthy or faulty) of any machines and critical assets. The model can then predict the exact fault for the faulty asset so that remedial maintenance can be carried out in a planned plant outage. Nowadays, the artificial intelligence (AI)-based machine learning (ML) model seems to be current trend to meet these requirements. Hence, the paper is also proposing such vibration-based faults diagnosis ML model through an experimental rotating rig. Here, the 2-Steps approach is used with the ML model to easy the industrial operation and maintenance process. TheStep-1 provides the information about the asset health status such as healthy or faulty. The Step-2 then identifies the exact nature of fault to aid the decision making for the fault rectification and maintenance activities to avoid the risk of failure and enhance the reliability.

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Section: Contains the Overall Results Insupporting

confidence: 73%

“…Earlier study [14] has optimised the vibration parameters to be used in the ML model for the reliable and accurate machine health diagnosis. These parameters are the combinations of time and frequency domain parameters of the measured vibration responses.…”

Section: Data Preparationmentioning

confidence: 99%

Robust vibration-based faults diagnosis machine learning model for rotating machines to enhance plant reliability

Espinoza-Sepulveda¹,

Sinha²

2021

Maint. reliab, cond. monit.

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“…Existing vibration data collected from an experimental rig [20] are used in the parameter optimisation study [21]. The experimental setup of the rig is shown in Figure 1a.…”

Section: Experimental Rig and Mode Shapesmentioning

confidence: 99%

“…In the experimental development of the vibration-based machine-learning (VML) model, a random number of samples are used from five rotor conditions and two operational speeds [21]. The healthy condition and 4 different rotor-related faults, i.e., misalignment, shaft bow, looseness in bearing pedestal, and rotor rub, are included.…”

Section: Experimental Datamentioning

confidence: 99%

Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines

Espinoza-Sepulveda

Sinha

2021

Machines

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Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems.

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“…In previous decades, traditional methods for landslide susceptibility assessment have relied on statistical-based approaches and data-driven approaches. For example, frequency ratio and index of entropy models [ 5 , 6 ], a logistic regression (LR) approach [ 7 , 8 , 9 ], and support vector machine (SVM) [ 10 , 11 , 12 , 13 ] are powerful approaches to predict potential landslides. However, these approaches require a large number of samples in order to yield high predicting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Tengtrairat

Woo

Parathai

et al. 2021

Sensors

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Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.

show abstract

Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines

Cited by 23 publications

References 27 publications

Robust vibration-based faults diagnosis machine learning model for rotating machines to enhance plant reliability

Robust vibration-based faults diagnosis machine learning model for rotating machines to enhance plant reliability

Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

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