Using Auxiliary Particle Filter to Estimate Remaining Useful Life

Coble, J.; Hines, J. Wesley; Xiao, Haiqiao

doi:10.13182/t122-32294

Cited by 6 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Monotonicity measures whether the change in features conforms to the trend of gradual increase or decrease [28]. Correlation measures the relativity between the features and running time [29].…”

Section: Monotonicity and Correlationmentioning

confidence: 99%

Remaining useful life prediction of bearings based on multiple-feature fusion health indicator and weighted temporal convolution network

Wang¹,

Zhang²,

Guo³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

In the Prognostic and Health Management, predicting the remaining useful life (RUL) of bearings is of great significance. The traditional RUL prediction methods have two disadvantages: 1) Health indicator (HI) is mainly constructed through expert experience and signal processing technology, which is lack of monotonicity and generalization; 2) In the RUL prediction, the time correlation of information is emphasized, but the influence of the variation amplitude and severity of vibration signal is ignored. Therefore, a method of bearings RUL prediction based on multiple features fusion HI (MFF-HI) and weighted temporal convolution network (WTCN) is proposed in this paper. MFF-HI is constructed by multiple features fusion depth network (MFFDN) with MISH activation function extracting and fusing the degradation information of bearing time-domain features; WTCN is established based on TCN and a new loss function Time-Mean Square Error (T-MSE) to reduce the error of network pickup in the bearing degradation stage. The NASA IMS dataset, IEEE PHM 2012 dataset and XJTU-SY dataset are used to verify the superiority of the method. The results show that this method can validly predict the RUL of bearings and has higher prediction accuracy.

show abstract

Section: Monotonicity and Correlationmentioning

confidence: 99%

Remaining useful life prediction of bearings based on multiple-feature fusion health indicator and weighted temporal convolution network

Wang¹,

Zhang²,

Guo³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…To quantitatively quantify the suitableness of degradation assessment of different HIs, the monotonicity, prognosability, and trendability are utilized to carry out a comparison. 55 These three evaluation indexes are a type of metrics used to quantify the suitableness and are defined as follows:…”

Section: Experimental Verification and Analysismentioning

confidence: 99%

Performance degradation assessment of wind turbine gearbox based on maximum mean discrepancy and multi-sensor transfer learning

Pan

Chen

Feng

et al. 2020

Structural Health Monitoring

View full text Add to dashboard Cite

Gearboxes are critical transmission components in the drivetrain of wind turbine, which have a dominant failure rate and the highest downtime loss in all wind turbine subsystems. However, load variations of wind turbine gearbox are far from smooth and usually nondeterministic, which result in inconsistent data distributions. To solve the problem, a novel performance degradation assessment and prognosis method based on maximum mean discrepancy is proposed to test the difference between data distributions and extract the characteristics of multi-source working conditions data. Besides, the increase in sensors will bring more difficulties to establish prediction models in real-world scenarios due to different installation locations. In view of this, a transfer learning strategy called joint distribution adaptation is utilized to adapt data distribution between multi-sensor signals. Nevertheless, the presence of background noise of wind turbine signals restricts the applicability of these algorithms in practice. To further reduce the distribution difference, a novel criterion is proposed to evaluate and measure the data distribution difference between known and tested working conditions based on the witness function of maximum mean discrepancy. The application and superiority of proposed methodology are validated using a wind turbine gearbox life-cycle test data set. Meanwhile, model comparison and cross-verification are conducted between conventional and proposed prediction models. The results indicate that the proposed method has a better performance in performance degradation assessment for wind turbine gearbox.

show abstract

“…They possess desirable features such as high stability, durability, and ease of use and maintenance, which have contributed to their widespread usage. However, under certain conditions, such as low insulation resistance, vibrations, overheating, etc., which can negatively impact electrical motors, they may suffer from faults or even become worthless if left unprotected [5], [6], [7]. As a result, it is crucial to detect faults in electrical motors as early as possible to protect workers, prevent substantial economic losses in manufacturing facilities, and reduce potential damage that could disrupt operations [8].…”

Section: Introductionmentioning

confidence: 99%

Advanced Dual RNN Architecture for Electrical Motor Fault Classification

Alkhanafseh,

Akinci,

Ayaz

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In recent years, there has been a remarkable increase in the usage of Deep Neural Networks (DNNs) for addressing and solving electrical field problems. This research primarily aims to present an advanced approach to classify different motor faults based on their time-series data by implementing a new Recurrent Neural Network (RNN) model that consists of mixed Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and two Fully Connected (FC) layers. The main idea of this study centers on developing one comprehensive model capable of categorizing primary motor faults. The proposed model is supposed to classify 10 different classes, extracted from the Machinery Fault Database (MaFaulDa), which are normal (no fault), vertical misalignment, horizontal misalignment, imbalance, overhang-ball, overhangcage, overhang-outer race, underhang-ball, underhang-outer race, and underhang-cage. Classifying 10 different situations can be considered as a notable classification problem. Additionally, the learning period did not include any data augmentation, which reflects the model's power in training over the available data. Significantly, the accuracy of the model is enhanced through setting precise values for hyperparameters, including network structure (number of layers and neurons), learning rate, regularization, optimizer type, number of epochs, and more. The obtained train-validation-test accuracies from the proposed model are 99.87%, 99.599%, and 99.48%, respectively. The accuracy of the model represents the highest accuracy between other publications. This advanced approach offers numerous advantages, including early-stage fault detection, improved robustness in industrial maintenance, and generating fast and intelligent alerts, thereby reducing the possible damages to the electrical instruments.

show abstract

Using Auxiliary Particle Filter to Estimate Remaining Useful Life

Cited by 6 publications

References 0 publications

Remaining useful life prediction of bearings based on multiple-feature fusion health indicator and weighted temporal convolution network

Remaining useful life prediction of bearings based on multiple-feature fusion health indicator and weighted temporal convolution network

Performance degradation assessment of wind turbine gearbox based on maximum mean discrepancy and multi-sensor transfer learning

Advanced Dual RNN Architecture for Electrical Motor Fault Classification

Contact Info

Product

Resources

About