Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple sensors.However,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating environments.In this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different sensors.Adversarial learning is further introduced to extract generalized sensor-invariant features.Through explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor malfunctions.The experimental results suggest the proposed approach is well suited for real industrial applications.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Remaining useful lifetime prediction for equipment based on nonlinear implicit degradation modeling

Nonlinearity and implicitness are common degradation features of the stochastic degradation equipment for prognostics.These features have an uncertain effect on the remaining useful life(RUL)prediction of the equipment.The current data-driven RUL prediction method has not systematically studied the nonlinear hidden degradation modeling and the RUL distribution function.This paper uses the nonlinear Wiener process to build a dual nonlinear implicit degradation model.Based on the historical measured data of similar equipment,the maximum likelihood estimation algorithm is used to estimate the fixed coefficients and the prior distribution of a random coefficient.Using the on-site measured data of the target equipment,the posterior distribution of a random coefficient and actual degradation state are step-by-step updated based on Bayesian inference and the extended Kalman filtering algorithm.The analytical form of the RUL distribution function is derived based on the first hitting time distribution.Combined with the two case studies,the proposed method is verified to have certain advantages over the existing methods in the accuracy of prediction.

A Novel Integrated Machine&Business Intelligence Framework for Sensor Data Analysis

Increased smart devices in various industries are creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data.Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment.The methodology involves data cleaning,preprocessing,basics statistics,outlier,and anomaly detection.Present study presents the prediction of RUL by using various Machine Learning models like Regression,Polynomial Regression,Random Forest,Decision Tree,XG Boost.Hyper Parameter Optimization is performed to find the optimal parameters for each variable.In each of the model for RUL prediction RMSE,MAE are compared.Outcome of the RUL prediction should be useful for decision maker to drive the business decision;hence Binary cclassification is performed,and business case analysis is performed.Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset.Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.

Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold

The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life(RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value,as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold(RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization(EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function(PDF) of the RUL is derived. Finally,the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.

Two-phase early prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussian process regression

Lithium-ion batteries(LIBs)are widely used in transportation,energy storage,and other fields.The prediction of the remaining useful life(RUL)of lithium batteries not only provides a reference for health management but also serves as a basis for assessing the residual value of the battery.In order to improve the prediction accuracy of the RUL of LIBs,a two-phase RUL early prediction method combining neural network and Gaussian process regression(GPR)is proposed.In the initial phase,the features related to the capacity degradation of LIBs are utilized to train the neural network model,which is used to predict the initial cycle lifetime of 124 LIBs.The Pearson coefficient’s two most significant characteristic factors and the predicted normalized lifetime form a 3D space.The Euclidean distance between the test dataset and each cell in the training dataset and validation dataset is calculated,and the shortest distance is considered to have a similar degradation pattern,which is used to determine the initial Dual Exponential Model(DEM).In the second phase,GPR uses the DEM as the initial parameter to predict each test set’s early RUL(ERUL).By testing four batteries under different working conditions,the RMSE of all capacity estimation is less than 1.2%,and the accuracy percentage(AP)of remaining life prediction is more than 98%.Experiments show that the method does not need human intervention and has high prediction accuracy.

A Regularized LSTM Method for Predicting Remaining Useful Life of Rolling Bearings

Rotating machinery is important to industrial production. Any failure of rotating machinery, especially the failure of rolling bearings, can lead to equipment shutdown and even more serious incidents. Therefore, accurate residual life prediction plays a crucial role in guaranteeing machine operation safety and reliability and reducing maintenance cost. In order to increase the forecasting precision of the remaining useful life(RUL) of the rolling bearing, an advanced approach combining elastic net with long short-time memory network(LSTM) is proposed, and the new approach is referred to as E-LSTM. The E-LSTM algorithm consists of an elastic mesh and LSTM, taking temporal-spatial correlation into consideration to forecast the RUL through the LSTM. To solve the over-fitting problem of the LSTM neural network during the training process, the elastic net based regularization term is introduced to the LSTM structure.In this way, the change of the output can be well characterized to express the bearing degradation mode. Experimental results from the real-world data demonstrate that the proposed E-LSTM method can obtain higher stability and relevant values that are useful for the RUL forecasting of bearing. Furthermore, these results also indicate that E-LSTM can achieve better performance.