Comparison of data Preprocessing Methods for Support Vector Regression based Remaining Useful Life Prediction

Gao, Kaidi; Cai, Zhuhua; Li, Zuxin; Zeng, Aigang; Jiang, Dongming; Xu, Jianping

doi:10.1109/dsit55514.2022.9943938

Cited by 1 publication

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the past decade, researchers have developed effective PHM measures to assess the health state of machinery. Support vector machines (SVMs) have successfully estimated the condition of rotating machinery and analyzed convex optimization problems in conjunction with empirical mode decomposition (EMD) [ 23 ]. As a special supervised machine learning algorithm, SVM can make use of both margin maximization and support vectors to achieve clear separation in classification and regression tasks.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Using an Optimal Blind Deconvolution Method and Hybrid Invertible Neural Network

Gao,

Ahmad,

Kim

2024

Sensors

View full text Add to dashboard Cite

This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). To optimize the weights of conventional ACYCBD, the proposed method utilizes a probability density function (PDF) of Monte Carlo to assess fault-related incipient changes in the vibration signal. Cross-entropy is used as a convergence criterion for denoising. Because the denoised signal carries information related to the health of the rotating machinery, a novel health index is calculated in the second step using the peak value and square of the arithmetic mean of the signal. The novel health index can change according to the degradation of the health state of the rotating bearing. To predict the remaining useful life of the bearing in the final step, the health index is used as input for a newly developed hybrid invertible neural network (HINN), which combines an invertible neural network and long short-term memory (LSTM) to forecast trends in bearing degradation. The proposed approach outperforms SVM, CNN, and LSTM methods in predicting the remaining useful life of bearings, showcasing RMSE values of 0.799, 0.593, 0.53, and 0.485, respectively, when applied to a real-world industrial bearing dataset.

show abstract

Section: Introductionmentioning

confidence: 99%