scholarly journals Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction

2019 ◽  
Author(s):  
Sunny Singh ◽  
Praneet Shiv ◽  
Atif Ahmed
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
System Failure ◽  
Mechanical Signal ◽  
Long Time ◽  
Data Points ◽  
Useful Life ◽  
Bearing System

In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.

scholarly journals Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction

2019 ◽  
Author(s):  
Sunny Singh ◽  
Praneet Shiv ◽  
Atif Ahmed
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
System Failure ◽  
Mechanical Signal ◽  
Long Time ◽  
Data Points ◽  
Useful Life ◽  
Bearing System

In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.

scholarly journals Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction

2019 ◽  
Author(s):  
Sunny Singh ◽  
Praneet Shiv ◽  
Atif Ahmed
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
System Failure ◽  
Mechanical Signal ◽  
Long Time ◽  
Data Points ◽  
Useful Life ◽  
Bearing System

In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.

scholarly journals In-situ remaining useful life prediction of aircraft auxiliary power unit based on quantitative analysis of on-wing sensing data

2020 ◽  
Vol 12 (3) ◽  
pp. 168781402091147
Author(s):  
Liansheng Liu ◽  
Qing Guo ◽  
Lulu Wang ◽  
Datong Liu
Keyword(s):  
Power Unit ◽  
Life Prediction ◽  
Health Management ◽  
Remaining Useful Life ◽  
Maintenance And Repair ◽  
Sensing Data ◽  
Auxiliary Power Unit ◽  
Auxiliary Power ◽  
Useful Life

The in-situ prognostics and health management of aircraft auxiliary power unit faces difficulty using the sparse on-wing sensing data. As the key technology of prognostics and health management, remaining useful life prediction of in-situ aircraft auxiliary power unit is hard to achieve accurate results. To solve this problem, we propose one kind of quantitative analysis of its on-wing sensing data to implement remaining useful life prediction of auxiliary power unit. Except the most important performance parameter exhaust gas temperature, the other potential parameters are utilized based on mutual information, which can be used as the quantitative metric. In this way, the quantitative threshold of mutual information for enhancing remaining useful life prediction result can be determined. The implemented cross-validation experiments verify the effectiveness of the proposed method. The real on-wing sensing data of auxiliary power unit for experiment are from China Southern Airlines Company Limited Shenyang Maintenance Base, which spends over $6.5 million on auxiliary power unit maintenance and repair each year for the fleet of over 500 aircrafts. Although the relative improvement is not too large, it is helpful to reduce the maintenance and repair cost.

scholarly journals Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878420 ◽  
Author(s):  
Shu-Fa Yan ◽  
Biao Ma ◽  
Chang-Song Zheng
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Cumulative Distribution ◽  
Kernel Principal Component Analysis ◽  
Degradation Index ◽  
Life Estimation ◽  
Power Shift ◽  
Useful Life

Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process–based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

Remaining useful life prediction for machinery by establishing scaled-corrected health indicators

Measurement ◽  
2020 ◽  
Vol 163 ◽  
pp. 108035
Author(s):  
Hanbo Yang ◽  
Zheng Sun ◽  
Gedong Jiang ◽  
Fei Zhao ◽  
Xuesong Mei
Keyword(s):  
Life Prediction ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals Health Indicators Construction and Remaining Useful Life Estimation for Concrete Structures Using Deep Neural Networks

2021 ◽  
Vol 11 (9) ◽  
pp. 4113
Author(s):  
Viet Tra ◽  
Tuan-Khai Nguyen ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Concrete Structure ◽  
Short Term Memory ◽  
Health Management ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Training Data ◽  
Statistical Parameters ◽  
Average Prediction Error ◽  
Degradation Data ◽  
Useful Life

Remaining useful life (RUL) prognosis is one of the most important techniques in concrete structure health management. This technique evaluates the concrete structure strength through determining the advent of failure, which is very helpful to reduce maintenance costs and extend structure life. Degradation information with the capability of reflecting structure health can be considered as a principal factor to achieve better prognosis performance. In traditional data-driven RUL prognosis, there are drawbacks in which features are manually extracted and threshold is defined to mark the specimen’s breakdown. To overcome these limitations, this paper presents an innovative SAE-DNN structure capable of automatic health indicator (HI) construction from raw signals. HI curves constructed by SAE-DNN have much better fitness metrics than HI curves constructed from statistical parameters such as RMS, Kurtosis, Sknewness, etc. In the next stage, HI curves constructed from training degradation data are then used to train a long short-term memory recurrent neural network (LSTM-RNN). The LSTM-RNN is utilized as a RUL predictor since its special gates allow it to learn long-term dependencies even when the training data is limited. Model construction, verification, and comparison are performed on experimental reinforced concrete (RC) beam data. Experimental results indicates that LSTM-RNN generally estimates more accurate RULs of concrete beams than GRU-RNN and simple RNN with the average prediction error cycles was less than half compared to those of the simple RNN.

scholarly journals A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 176 ◽  
Author(s):  
David Verstraete ◽  
Enrique Droguett ◽  
Mohammad Modarres
Keyword(s):  
Sensor Fusion ◽  
Asset Management ◽  
Life Prediction ◽  
Health Management ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Useful Life

Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.

scholarly journals Remaining useful life prediction for ball bearings based on health indicators

2019 ◽  
Vol 261 ◽  
pp. 02003
Author(s):  
Zeina Al Masry ◽  
Patrick Schaible ◽  
Noureddine Zerhouni ◽  
Christophe Varnier
Keyword(s):  
Probabilistic Model ◽  
Life Prediction ◽  
Simulated Data ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
Prognostic Models ◽  
Ball Bearings ◽  
Scientific Problem ◽  
Useful Life

Uncertainty in remaining useful life (RUL) prediction is nowadays a scientific problem that occupies industrials. Many prognostic models have been developed to respond to this issue from probabilistic to non-probabilistic approaches. In this paper, we deal with a non- probabilistic model for RUL prediction. For this purpose, we propose a model, which is based on health indicators information, that allows to estimate the RUL of ball bearings. The method is applied to simulated data provided by the PRONOSTIA platform designed and realized at AS2M department of FEMTO- ST Institute.

scholarly journals Health indicator construction and remaining useful life prediction for space Stirling cryocooler

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989673
Author(s):  
Lei Song ◽  
Haoran Liang ◽  
Wei Teng ◽  
Lili Guo
Keyword(s):  
Life Prediction ◽  
Thermal Environment ◽  
Health Indicators ◽  
Degradation Process ◽  
Space Mission ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Indicator Method ◽  
Aerospace Applications ◽  
Useful Life

Stirling cryocoolers are widely used to refrigerate significant facilities in military and aerospace applications. However, under the influences of high-frequency piston motion and thermal environment deterioration, the refrigerating performance of Stirling cryocoolers will worsen inevitably, thus affecting the successful accomplishment of space mission. In this article, a methodology on assessing the performance of space Stirling cryocoolers is proposed, which involves the analysis of the failure mechanism, health indicator construction and remaining useful life prediction of the cryocooler. The potential factors affecting the refrigerating performance are discussed first. In view of these, three health indicators representing the degradation process of cryocoolers are constructed and then a multi-indicator method based on particle filter is proposed for remaining useful life prediction. Finally, the proposed method is validated by a Stirling cryocooler from one retired aircraft, and the results show that the constructed health indicators and remaining useful life prediciton approaches are effective for performance assessment of Stirling cryocooler.

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