scholarly journals Machine Learning Approach to Diesel Engine Health Prognostics using Engine Controller Data

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Steve Nixon ◽  
Ryan Weichel ◽  
Karl Reichard ◽  
James Kozlowski
Keyword(s):  
Diesel Engine ◽  
Failure Mode ◽  
Diesel Engines ◽  
Failure Modes ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Data Sets ◽  
Classifier Selection ◽  
Useful Life

Many military assets such as surface ships and ground vehicles use diesel engines as their prime movers, and accurately estimating remaining useful life has a high value for enabling predictive maintenance and improving fleet logistics. Most of these diesel engines are already equipped with an array of sensors and digital data busses to support the function of the integrated electronic control module (ECM). There are cost advantages to developing predictive analytics and prognostics using existing embedded sensors. This paper describes a hybrid approach to predictive capabilities that utilizes multiple techniques for the implementation of embedded prognostics using existing sensors. One of the challenges is the fidelity of the data. This paper describes an automated approach to feature and classifier selection for hybrid prognostics. Maintenance records with associated diesel engine sensor data for several different engine classes were acquired, which enabled the training data sets to be organized by failure modes. To help prevent false positives, some filtering of the maintenance logs was required to only include those records likely to be associated with the selected failure mode sensor data sets. The classifier-based, datadriven approach essentially maps multiple channels of the sensor data into subspaces trained to classify multiple distinct failure modes. The intent of this step is to enable fault isolation by quantitatively determining which failure mode class the data best fits statistically. The remaining useful life estimate is provided by tracking the temporal path of the data from the healthy engine classification to one of the known failure mode classes using engine load-hours as the metric for the prognostics.

scholarly journals An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Ogechukwu Alozie ◽  
Yi-Guang Li ◽  
Xin Wu ◽  
Xingchao Shong ◽  
Wencheng Ren
Keyword(s):  
Gas Turbine ◽  
Fault Isolation ◽  
Performance Model ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Sensor Data ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Degradation Models ◽  
Useful Life

This paper presents an adaptive framework for prognostics in civil aero gas turbine engines, which incorporates both performance and degradation models, to predict the remaining useful life of the engine components that fail predominantly by gradual deterioration over time. Sparse information about the engine configuration is used to adapt a performance model, which serves as a baseline for implementing optimum sensor selection, operating data correction, fault isolation, noise reduction and component health diagnostics using nonlinear Gas Path Analysis (GPA). Degradation models, which describe the progression of faults until failure, are then applied to the diagnosed component health indices from previous run-to-failure cases. These models constitute a training library from which fitness evaluation to the current test case is done. The final remaining useful life (RUL) prediction is obtained as a weighted sum of individually evaluated RULs for each training case. This approach is validated using dataset generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software, which comprises both training and testing instances of run-to-failure sensor data for a turbofan engine, some of which are obtained at different operating conditions and for multiple fault modes. The results demonstrate the capability of improved prognostics of faults in aircraft engine turbomachinery using models of system behavior, with continuous health monitoring data.

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 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.

scholarly journals A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

2019 ◽  
Vol 9 (22) ◽  
pp. 4813 ◽  
Author(s):  
Hanbo Yang ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Zheng Sun ◽  
Xuesong Mei
Keyword(s):  
Time Windows ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Time Interval ◽  
Kernel Module ◽  
Initial Stage ◽  
Research Task ◽  
Consecutive Time ◽  
Useful Life

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

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

Pig Farm Environment Sensor Data Correlation and Heatmap Analysis for Predicting Sensor Remaining Useful Life✱

2020 ◽  
Author(s):  
Ji Hoon Lee ◽  
Seung Min Oh ◽  
Yeong Gwang Kim ◽  
Dong Su Lee ◽  
Akm Ashiquzzaman ◽  
...  
Keyword(s):  
Remaining Useful Life ◽  
Sensor Data ◽  
Data Correlation ◽  
Pig Farm ◽  
Useful Life

Diamond-Coated Mechanical Seal Remaining Useful Life Prediction Based on Convolution Neural Network

2019 ◽  
Vol 34 (05) ◽  
pp. 2051007
Author(s):  
Zhibin Lin ◽  
Hongli Gao ◽  
Erqing Zhang ◽  
Weiqing Cao ◽  
Kesi Li
Keyword(s):  
Nuclear Power ◽  
Signal To Noise Ratio ◽  
Prediction Method ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Mechanical Seal ◽  
Process Industries ◽  
Petroleum Chemical ◽  
Useful Life

Reliable remaining useful life (RUL) prediction of industrial equipment key components is of considerable importance in condition-based maintenance to avoid catastrophic failure, promote reliability and reduce cost during the production. Diamond-coated mechanical seal is one of the most critical wearing components in petroleum chemical, nuclear power and other process industries. Estimating the RUL is of critical importance. We consider the data-driven approaches for diamond-coated mechanical seal RUL estimation based on AE sensor data, since it is difficult to construct an explicit mathematical degradation model of seal. The challenges of this work are dealing with the noisy AE sensor data and modeling the degradation process with fluctuation. Faced with these challenges, we propose a pipeline method CDF-CNN to estimate the RUL for mechanical seal: WPD-KLD to raise the signal-to-noise ratio, novel CDF-based statistics to represent seal degradation process and CNN structure to estimate RUL. To acquire AE sensor data, several diamond-coated seals are tested from new to failure in three working conditions. Experimental results demonstrate that the proposed method can accurately predict the RUL of diamond-coated mechanical seal based on AE signals. The proposed prediction method can be generalized to other various mechanical assets.

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 An Autoencoder Gated Recurrent Unit for Remaining Useful Life Prediction

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1155
Author(s):  
Yi-Wei Lu ◽  
Chia-Yu Hsu ◽  
Kuang-Chieh Huang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Development Trend ◽  
Production Equipment ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Smart Manufacturing ◽  
Useful Life ◽  
Gated Recurrent Unit

With the development of smart manufacturing, in order to detect abnormal conditions of the equipment, a large number of sensors have been used to record the variables associated with production equipment. This study focuses on the prediction of Remaining Useful Life (RUL). RUL prediction is part of predictive maintenance, which uses the development trend of the machine to predict when the machine will malfunction. High accuracy of RUL prediction not only reduces the consumption of manpower and materials, but also reduces the need for future maintenance. This study focuses on detecting faults as early as possible, before the machine needs to be replaced or repaired, to ensure the reliability of the system. It is difficult to extract meaningful features from sensor data directly. This study proposes a model based on an Autoencoder Gated Recurrent Unit (AE-GRU), in which the Autoencoder (AE) extracts the important features from the raw data and the Gated Recurrent Unit (GRU) selects the information from the sequences to forecast RUL. To evaluate the performance of the proposed AE-GRU model, an aircraft turbofan engine degradation simulation dataset provided by NASA was used and a comparison made of different recurrent neural networks. The results demonstrate that the AE-GRU is better than other recurrent neural networks, such as Long Short-Term Memory (LSTM) and GRU.

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.

Sign in / Sign up

Export Citation Format

Share Document