Fault diagnosis of key components in the rotating machinery based on Fourier transform multi-filter decomposition and optimized LightGBM

Zhang, Changhe; Kong, Li; Xu, Qi; Pan, Hao

doi:10.1088/1361-6501/aba93b

Cited by 24 publications

(14 citation statements)

References 48 publications

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“…Maldonado et al [14] proposed an embedded feature selection for SVM classification based on imbalanced datasets, and Liu et al [15] developed a weighted Gini index as the embedded feature selection method and compared it with chi-square and Fstatistic and applied the selected features to a decision tree as a classifier for imbalanced data. Zhang et al [16] performed fault diagnosis of key components in rotating machinery using joint mutual information maximization to reduce the redundant features and a light gradient boosting decision tree to rank the candidate features and obtain classification results. The embedded methods are easy to use in that they can simultaneously select features and learn models, and are known to be more accurate than filter methods and tend to be less overfitting.…”

Section: Embedded Methodsmentioning

confidence: 99%

Stable Hybrid Feature Selection Method for Compressor Fault Diagnosis

et al. 2021

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Faulty compressors must be detected in advance to speed up the quality control process of the compressor's performance. Machine learning models have recently been used as fault classification models to distinguish between normal and abnormal compressors, facilitating more sophisticated fault detection methods than those in the past. However, very few studies have been conducted on accurate and efficient feature selection, despite its high importance. Therefore, this study proposes a new hybrid method that combines the merits of existing feature methods, filter and wrapper methods, to obtain a stable, accurate, and efficient fault classification model. For this, three types of filtering methods with different characteristics, such as chi-square, extra tree classifier, and correlation matrix, are used to derive the high-ranked features and then create a powerful feature set consisting of their union sets. Subsequently, using the wrapper method, one combination of features with the highest classification accuracy was selected among all the combinations of features in the union feature set. Using two experimental examples and one numerical example with different types and numbers of data, the robustness and accuracy of the proposed method were verified through comparison with the existing filter methods by combining three classification models: support vector machine, K-nearest neighbor, and multi-layer perceptron.

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Section: Embedded Methodsmentioning

confidence: 99%

Stable Hybrid Feature Selection Method for Compressor Fault Diagnosis

et al. 2021

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“…Applied in image recognition recently, as an example, LightGBM has been validated as an effective method for distinguishing COVID-19 from bacterial pneumonia [29]. In fault diagnosis, LightGBM was used in diagnosing shipboard medium-voltage DC power system faults [30], and rotating machinery faults [31].…”

Section: Diagnosis Model Based On Lightgbmmentioning

confidence: 99%

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

et al. 2021

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In the process of Unmanned Aerial Vehicle (UAV) flight testing, plenty of compound faults exist, which could be composed of concurrent single faults or over-limit states alarmed by Built-In-Test (BIT) equipment. At present, there still lacks a suitable automatic labeling approach for UAV flight data, effectively utilizing the information of the BIT record. The performance of the originally employed flight data-driven fault diagnosis models based on machine learning needs to be improved as well. A compound fault labeling and diagnosis method based on actual flight data and the BIT record of the UAV during flight test phase is proposed, through labeling the flight data with compound fault modes corresponding to concurrent single faults recorded by the BIT system, and upgrading the original diagnosis model based on Gradient Boosting Decision Tree (GBDT) and Fully Convolutional Network (FCNN), to eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and modified Convolutional Neural Network (CNN). The experimental results based on actual test flight data show that the proposed method could effectively label the flight data and obtain a significant improvement in diagnostic performance, appearing to be practical in the UAV test flight process.

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“…Microsoft proposed the LightGBM (Light Gradient Boosting Machine), an exceedingly fast gradient boosting framework, to improve the model training speed when processing large-scale data [31]. In fault diagnosis, LightGBM has been used in diagnosing shipboard medium-voltage DC power system faults [32], rotating machinery faults [33], and classic bearing faults [34].…”

Section: Lightgbmmentioning

confidence: 99%

A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

et al. 2021

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In the fault diagnosis of UAVs, extremely imbalanced data distribution and vast differences in effects of fault modes can drastically affect the application effect of a data-driven fault diagnosis model under the limitation of computing resources. At present, there is still no credible approach to determine the cost of the misdiagnosis of different fault modes that accounts for the interference of data distribution. The performance of the original cost-insensitive flight data-driven fault diagnosis models also needs to be improved. In response to this requirement, this paper proposes a two-step ensemble cost-sensitive diagnosis method based on the operation and maintenance data of UAV. According to the fault criticality from FMECA information, we defined a misdiagnosis hazard value and calculated the misdiagnosis cost. By using the misdiagnosis cost, a static cost matrix could be set to modify the diagnosis model and to evaluate the performance of the diagnosis results. A two-step ensemble cost-sensitive method based on the MetaCost framework was proposed using stratified bootstrapping, choosing LightGBM as meta-classifiers, and adjusting the ensemble form to enhance the overall performance of the diagnosis model and reduce the occupation of the computing resources while optimizing the total misdiagnosis cost. The experimental results based on the KPG component data of a large fixed-wing UAV show that the proposed cost-sensitive model can effectively reduce the total cost incurred by misdiagnosis, without putting forward excessive requirements on the computing equipment under the condition of ensuring a certain overall level of diagnosis performance.

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Fault diagnosis of key components in the rotating machinery based on Fourier transform multi-filter decomposition and optimized LightGBM

Cited by 24 publications

References 48 publications

Stable Hybrid Feature Selection Method for Compressor Fault Diagnosis

Stable Hybrid Feature Selection Method for Compressor Fault Diagnosis

A Compound Fault Labeling and Diagnosis Method Based on Flight Data and BIT Record of UAV

A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV

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