Optimized Random Forest Model for Remaining Useful Life Prediction of Experimental Bearings

Alfarizi, Muhammad Gibran; Tajiani, Bahareh; Vatn, Jørn; Yin, Shen

doi:10.1109/tii.2022.3206339

Cited by 25 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to its ability for dealing with high-dimensional data, nonlinear relationships, and interactions between features, RF is one of the popular machine learning algorithms. 24 Numerous industries have made an extensive use of RF for applications involving predictive maintenance of bearings in rotating machinery, 26 and failure detection of wind turbine gearboxes. 27 It is a popular option for predictive maintenance applications due to its capacity to handle high-dimensional data, nonlinear relationships, and interactions between features.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…Numerous industries have made an extensive use of RF for applications involving predictive maintenance of bearings in rotating machinery, 26 and failure detection of wind turbine gearboxes. 27 It is a popular option for predictive maintenance applications due to its capacity to handle high-dimensional data, nonlinear relationships, and interactions between features.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Mohd fadzil,

Razali,

Zabiri

et al. 2024

ACS Omega

View full text Add to dashboard Cite

Lubricants are important fluids and are commonly used to suppress friction between two metallic surfaces and as a medium for heat transportation. In an industrial plant considered in this study, the base oil mode changes can only be detected based on the kinematic viscosity values obtained using lab analysis. Since the lab analysis data are only available every 8 h, detecting the change in the production modes for 4, 6, and 10 cSt and the transitions among them are significantly delayed, causing unnecessary off-spec products that have to be directed to the slopping tank. In this paper, the innovativeness of the work comes from the idea of trying to unravel the underlying pattern of the plant data that correlate to the changes in the base oil modes and using that to classify hourly the kinematic viscosity values. Hence, a novel industrial application is presented to predict the class of base oil mode change on an hourly basis that can significantly reduce the losses in terms of off spec products and sloping tank wastes. The modes are segregated into three classes based on the values of kinematic viscosity. The classes are C-1 (4 cSt), C-2 (6 cSt), and C-3 (10 cSt). Anything in between the stipulated thresholds is called transition [T-12 (C-1 to C-2), T-21(C-2 to C-1), T-23 (C-2 to C-3), T-31 (C-3 to C-1), and T-32 (C-3 to C-2)]. To unravel the pattern, principal component analysis (PCA) is utilized on 42,000 operating plant data. After a thorough analysis, the third principal component provides the highest correlation to the eight classes of the base oil mode changes [C-1 (4 cSt), C-2 (6 cSt), and C-3 (10 cSt) and the transitions T-12 (C-1 to C-2), T-21(C-2 to C-1), T-23 (C-2 to C-3), T-31 (C-3 to C-1), and T-32 (C-3 to C-2)]. This third principal component is then utilized together with plant process variable values as inputs to four machine learning models, namely, XGBOOST, Random Forest, and CatBoost algorithms to predict the mode of the base oil hourly. The overall comparison analysis shows that utilizing the XGBoost algorithm for the prediction of the eight classes of the base oil modes at a faster hourly rate results in the most consistent classification accuracy of 92.96% for the test set and 89.22% in the deployment set. This capability to predict the mode change in the hourly basis can significantly reduce the losses in terms of off spec products in the production line.

show abstract

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Mohd fadzil,

Razali,

Zabiri

et al. 2024

ACS Omega

View full text Add to dashboard Cite

show abstract

“…The ratio of the duration between the moment corresponding to the i th spectrogram and the bearing failure time to the overall duration from the beginning of the bearing to the bearing failure time is referred to as the remaining useful life of the bearing ( y i ). The formula for y i is given by [ 28 , 29 , 30 ]

where n represents the total number of time–frequency spectra in this dataset, and i is the label or index of the current moment’s time–frequency spectrum.…”

Section: Actual Data Analysismentioning

confidence: 99%

Life Prediction of Rolling Bearing Based on Optimal Time–Frequency Spectrum and DenseNet-ALSTM

Chen,

Yan,

Dong

et al. 2024

Sensors

View full text Add to dashboard Cite

To address the challenges faced in the prediction of rolling bearing life, where temporal signals are affected by noise, making fault feature extraction difficult and resulting in low prediction accuracy, a method based on optimal time–frequency spectra and the DenseNet-ALSTM network is proposed. Firstly, a signal reconstruction method is introduced to enhance vibration signals. This involves using the CEEMDAN deconvolution method combined with the Teager energy operator for signal reconstruction, aiming to denoise the signals and highlight fault impacts. Subsequently, a method based on the snake optimizer (SO) is proposed to optimize the generalized S-transform (GST) time–frequency spectra of the enhanced signals, obtaining the optimal time–frequency spectra. Finally, all sample data are transformed into the optimal time–frequency spectrum set and input into the DenseNet-ALSTM network for life prediction. The comparison experiment and ablation experiment show that the proposed method has high prediction accuracy and ideal prediction performance. The optimization terms used in different contexts in this paper are due to different optimization methods, specifically the CEEMDAN method.

show abstract

“…Within data-driven approaches, machine learning techniques are recognized as powerful and efficient solutions for adaptively modeling the degradation process using measurement data. Various traditional machine learning methods have been employed in this context, such as random forest [11], support vector machine [12], and artificial neural networks [13,14].…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of bearings using a trend memory attention-based GRU network

Li,

Fan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Remaining useful life prediction of bearings holds significant importance in enhancing the reliability and durability of rotating machinery. Bearings undergo a gradual degradation process that unfolds over multiple stages. In this paper, a novel framework for forecasting the remaining useful life of bearings is put forward, which includes the construction of a health indicator with a stage division algorithm and the estimation of the health indicator using a new trend memory attention-based gated recurrent unit (TMAGRU). The stage division algorithm, based on the K-Means++ algorithm and angle recognition algorithm, is introduced to distinguish the degradation stage based on the health indicator. Inspired by the double exponential smoothing technique and attention mechanism, the proposed TMAGRU network effectively incorporates both the historical health information in the slow degradation stage and its trend. Experimental results conducted on IEEE PHM Challenge 2012 dataset and XJTU-SY dataset demonstrate the superior predictive performance of the proposed approach compared to several state-of-the-art predictive networks.

show abstract

Optimized Random Forest Model for Remaining Useful Life Prediction of Experimental Bearings

Cited by 25 publications

References 31 publications

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models

Life Prediction of Rolling Bearing Based on Optimal Time–Frequency Spectrum and DenseNet-ALSTM

Remaining useful life prediction of bearings using a trend memory attention-based GRU network

Contact Info

Product

Resources

About