Health State Assessment and Lifetime Prediction Based on Unsupervised State Estimation

2019 ◽  
Author(s):  
Rosmawati Jihin ◽  
Dirk Söffker
Keyword(s):  
State Estimation ◽  
Clustering Algorithm ◽  
State Machine ◽  
State Assessment ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Hydraulic Pressure ◽  
Health State ◽  
Estimation Process ◽  
Lifetime Model

Abstract Assessment of system health and prediction of remaining useful life can be performed effectively through the evaluation of degradation levels configured by multiple states. Commonly, degradation progression is modeled according to the specific configuration using existing algorithms with assuming numbers and state conditions. However, due to the complexity, especially in the case of a system with multiple hidden states, the proper configuration is hard to assign and to identify. The need for unsupervised state estimation process to assist degradation modeling preventing under or over assumption becomes obvious. Among the existing approaches is the application of clustering methods to classify data and to estimate the number of degradation states might exist. However, integration into the lifetime prediction framework is still infancy and often not considered. Therefore, in this work, a previously developed state machine lifetime model is extended to allow flexibility in configuring state topology based on K-means clustering algorithm and cluster validity index for the optimal number of states identification. Combining unsupervised state estimation process with a new state machine lifetime model has transformed it into a semi-supervised prognostic approach. For validation, hydraulic pressure data from tribology experiment are deployed for training and test the algorithm. Based on the evaluation, this approach demonstrates the ability to improve health state assessment and lifetime prediction in a more flexible way to address the variability in the system.

Failure Modes Mechanisms Effects Analysis for Refrigeration Device

2013 ◽  
Vol 288 ◽  
pp. 69-74 ◽  
Author(s):  
Ren Xiao Xu ◽  
Yang Liu
Keyword(s):  
Life Cycle ◽  
Failure Modes ◽  
Life Cycle Management ◽  
State Assessment ◽  
Remaining Useful Life ◽  
Automatic Identification ◽  
Health State ◽  
Useful Life ◽  
Using Data ◽  
Fusion Approach

FMMEA (failure mode, mechanisms, and effects analysis) is an effective tool for the life-cycle management of products and devices. We conducted an FMMEA for a refrigeration device at the request of a corporation. This paper demonstrates the process of our analysis of the compressor by employing Ganesan’s methodology. The results are listed in a table, including the physics of failures, risk priorities and parameters for monitoring. This paper also provides health-state assessment approaches based on FMMEA results and values of relevant parameters using fusion approach. Such assessment can be used for remaining useful life (RUL) estimation. Additionally, the paper illustrates our approach of computer-program-based automatic identification of failure using data of parameters retrieved from sensors.

Remaining useful life prediction of rolling element bearings based on health state assessment

2015 ◽  
Vol 230 (2) ◽  
pp. 314-330 ◽  
Author(s):  
Zhiliang Liu ◽  
Ming J Zuo ◽  
Yong Qin
Keyword(s):  
Regression Model ◽  
Hybrid Approach ◽  
State Assessment ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Health State ◽  
Rolling Element Bearings ◽  
Local Regression ◽  
Rolling Element ◽  
Useful Life

Instead of looking for an overall regression model for remaining useful life (RUL) prediction, this paper proposes a RUL prediction framework based on multiple health state assessment that divides the entire bearing life into several health states where a local regression model can be built individually. A hybrid approach consisting of both unsupervised learning and supervised learning is proposed to automatically estimate the real-time health state of a bearing in cases with no prior knowledge available. Support vector machine is the main technology adopted to implement health state assessment and RUL prediction. Experimental results on accelerated degradation tests of rolling element bearings demonstrate the effectiveness of the proposed framework.

scholarly journals State Estimation for Cooperative Lateral Vehicle Following Using Vehicle-to-Vehicle Communication

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 651
Author(s):  
Wouter Schinkel ◽  
Tom van der Sande ◽  
Henk Nijmeijer
Keyword(s):  
State Estimation ◽  
Cooperative Control ◽  
The State ◽  
State Estimator ◽  
Estimation Process ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Control Schemes ◽  
Input Signals ◽  
Vehicle To Vehicle Communication

A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to ensure accurate and stable vehicle following behavior. Control schemes for the cooperative control of longitudinal and lateral vehicle dynamics generally require vehicle state information about the lead vehicle, which in some cases cannot be accurately measured. Including vehicle-to-vehicle communication in the state estimation process can provide the required input signals for the practical implementation of cooperative control schemes. This study is focused on demonstrating the benefits of using vehicle-to-vehicle communication in the state estimation of a lead and following vehicle via simulations. The state estimator, which uses a cascaded Kalman filtering process, takes the operating frequencies of different sensors into account in the estimation process. Simulation results of three different driving scenarios demonstrate the benefits of using vehicle-to-vehicle communication as well as the attenuation of measurement noise. Furthermore, in contrast to relying on low frequency measurement data for the input signals of cooperative control schemes, the state estimator provides a state estimate at every sample.

New health-state assessment model based on belief rule base with interpretability

2021 ◽  
Vol 64 (7) ◽  
Author(s):  
Zhijie Zhou ◽  
You Cao ◽  
Guanyu Hu ◽  
Youmin Zhang ◽  
Shuaiwen Tang ◽  
...  
Keyword(s):  
State Assessment ◽  
Assessment Model ◽  
Rule Base ◽  
Health State ◽  
Model Based ◽  
Belief Rule Base

scholarly journals Predicting Remaining Useful Life of Rolling Bearings Based on Reliable Degradation Indicator and Temporal Convolution Network with the Quantile Regression

2021 ◽  
Vol 11 (11) ◽  
pp. 4773
Author(s):  
Qiaoping Tian ◽  
Honglei Wang
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Health State ◽  
Rolling Bearings ◽  
Data Set ◽  
Time Frequency ◽  
Useful Life ◽  
Degradation Indicator

High precision and multi information prediction results of bearing remaining useful life (RUL) can effectively describe the uncertainty of bearing health state and operation state. Aiming at the problem of feature efficient extraction and RUL prediction during rolling bearings operation degradation process, through data reduction and key features mining analysis, a new feature vector based on time-frequency domain joint feature is found to describe the bearings degradation process more comprehensively. In order to keep the effective information without increasing the scale of neural network, a joint feature compression calculation method based on redefined degradation indicator (DI) was proposed to determine the input data set. By combining the temporal convolution network with the quantile regression (TCNQR) algorithm, the probability density forecasting at any time is achieved based on kernel density estimation (KDE) for the conditional distribution of predicted values. The experimental results show that the proposed method can obtain the point prediction results with smaller errors. Compared with the existing quantile regression of long short-term memory network(LSTMQR), the proposed method can construct more accurate prediction interval and probability density curve, which can effectively quantify the uncertainty of bearing running state.

scholarly journals Cross-Modal Health State Estimation

2018 ◽  
Author(s):  
Nitish Nag ◽  
Vaibhav Pandey ◽  
Preston J. Putzel ◽  
Hari Bhimaraju ◽  
Srikanth Krishnan ◽  
...  
Keyword(s):  
State Estimation ◽  
Health State

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

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