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 Estimation of Aircraft Engines Based on Deep Convolution Neural Network and LightGBM Combination Model

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Lijun Liu ◽  
Lan Wang ◽  
Zhen Yu
Keyword(s):  
Time Window ◽  
Health Management ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Gradient Boosting ◽  
Data Set ◽  
Light Gradient ◽  
Model Combining ◽  
Aero Engine ◽  
Useful Life

AbstractAccurately predicting the remaining useful life (RUL) of aero-engines is of great significance for improving the reliability and safety of aero-engine systems. Because of the high dimension and complex features of sensor data in RUL prediction, this paper proposes a model combining deep convolution neural networks (DCNN) and the light gradient boosting machine (LightGBM) algorithm to estimate the RUL. Compared with traditional prognostics and health management (PHM) techniques, signal processing of raw sensor data and prior expertise are not required. The procedure is shown as follows. First, the time window of raw data of the aero-engine is used as the input of DCNN after normalization. The role of DCNN is to extract information from the input data. Second, considering the limitations of the fully connected layer of DCNN, we replace it with a strong classifier-LightGBM to improve the accuracy of prediction. Finally, to prove the effectiveness of the proposed method, we conducted some experiments on the C-MAPSS data set provided by NASA, and obtained good accuracy. By comparing the prediction effect with other commonly used algorithms on the same data set, the proposed algorithm has obvious advantages.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

2020 ◽  
pp. 1-1
Author(s):  
Naipeng Li ◽  
Yaguo Lei ◽  
Nagi Gebraeel ◽  
Zhijian Wang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Sensor Data ◽  
Useful Life

Remaining useful life prediction based on a multi-sensor data fusion model

2021 ◽  
Vol 208 ◽  
pp. 107249
Author(s):  
Naipeng Li ◽  
Nagi Gebraeel ◽  
Yaguo Lei ◽  
Xiaolei Fang ◽  
Xiao Cai ◽  
...  
Keyword(s):  
Data Fusion ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Fusion Model ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Useful Life

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 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 A novel deep capsule neural network for remaining useful life estimation

2019 ◽  
Vol 234 (1) ◽  
pp. 151-167 ◽  
Author(s):  
Andrés Ruiz-Tagle Palazuelos ◽  
Enrique López Droguett ◽  
Rodrigo Pascual
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Computation Time ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Fault Diagnostics ◽  
Data Set ◽  
Life Estimation ◽  
Useful Life ◽  
Fault Prognostics

With the availability of cheaper multi-sensor systems, one has access to massive and multi-dimensional sensor data for fault diagnostics and prognostics. However, from a time, engineering and computational perspective, it is often cost prohibitive to manually extract useful features and to label all the data. To address these challenges, deep learning techniques have been used in the recent years. Within these, convolutional neural networks have shown remarkable performance in fault diagnostics and prognostics. However, this model present limitations from a prognostics and health management perspective: to improve its feature extraction generalization capabilities and reduce computation time, ill-based pooling operations are employed, which require sub-sampling of the data, thus loosing potentially valuable information regarding an asset’s degradation process. Capsule neural networks have been recently proposed to address these problems with strong results in computer vision–related classification tasks. This has motivated us to extend capsule neural networks for fault prognostics and, in particular, remaining useful life estimation. The proposed model, architecture and algorithm are tested and compared to other state-of-the art deep learning models on the benchmark Commercial Modular Aero Propulsion System Simulation turbofans data set. The results indicate that the proposed capsule neural networks are a promising approach for remaining useful life prognostics from multi-dimensional sensor data.

Remaining Useful Life (RUL) Prediction of Rolling Element Bearing Using Random Forest and Gradient Boosting Technique

2018 ◽  
Author(s):  
Sangram Patil ◽  
Aum Patil ◽  
Vishwadeep Handikherkar ◽  
Sumit Desai ◽  
Vikas M. Phalle ◽  
...  
Keyword(s):  
Random Forest ◽  
Learning Curves ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Data Driven ◽  
Gradient Boosting ◽  
Data Set ◽  
Target Values ◽  
Rolling Element ◽  
Useful Life

Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially expensive maintenance program. Accordingly, Remaining Useful Life (RUL) prediction based on artificial intelligence is an attractive methodology for several researchers. In this study, data-driven condition monitoring approach is implemented for predicting RUL of bearing under a certain load and speed. The approach demonstrates the use of ensemble regression techniques like Random Forest and Gradient Boosting for prediction of RUL with time-domain features which are extracted from given vibration signals. The extracted features are ranked using Decision Tree (DT) based ranking technique and training and testing feature vectors are produced and fed as an input to ensemble technique. Hyper-parameters are tuned for these models by using exhaustive parameter search and performance of these models is further verified by plotting respective learning curves. For the present work FEMTO bearing data-set provided by IEEE PHM Data Challenge 2012 is used. Weibull Hazard Rate Function for each bearing from learning data set is used to find target values i.e. projected RUL of the bearings. Results of proposed models are compared with well-established data-driven approaches from literature and are found to be better than all the models applied on this data-set, thereby demonstrating the reliability of the proposed model.

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