System-level prognostics and health management: A graph convolutional network–based framework

2020 ◽  
pp. 1748006X2093576 ◽  
Author(s):  
Andrés Ruiz-Tagle Palazuelos ◽  
Enrique López Droguett
Keyword(s):  
Deep Learning ◽  
Reliability Analysis ◽  
Health Management ◽  
Sensor Data ◽  
System Level ◽  
Health State ◽  
Prognostics And Health Management ◽  
Component Level ◽  
Convolutional Network ◽  
State Diagnosis

Sensing technologies have been used to gather massive amounts of data to improve system reliability analysis with the use of deep learning. Their use has been mainly focused on specific components or for the whole system, resulting in a drawback when dealing with complex systems as the interactions among components are not explicitly taken into account. Here, we propose a system-level prognostics and health management framework based on geometrical deep learning where a system, its components with their interactions, and sensor data are represented as a graph. This enables reliability analysis at different hierarchical levels by means of (1) a system-level module for system health diagnosis and prognosis based on embeddings of the system’s learned features from a graph convolutional network; (2) a component-level module based on a deep graph convolutional network for health state diagnosis for the system’s components; (3) a component interactions module based on a graph convolutional network autoencoder that allows for the identification of interactions among components when the system is in a degraded state. The framework is exemplified via a case study involving a chlorine dioxide generation system, in which it is shown that integrating both components’ interactions and sensor data in the form of a graph improves health state diagnosis capabilities.

scholarly journals Challenges and Opportunities of System-Level Prognostics

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7655
Author(s):  
Seokgoo Kim ◽  
Joo-Ho Choi ◽  
Nam H. Kim
Keyword(s):  
Health Management ◽  
System Level ◽  
Prognostics And Health Management ◽  
Health Index ◽  
System Operation ◽  
Component Level ◽  
Industrial Systems ◽  
Multiple Components ◽  
Challenges And Opportunities ◽  
Essential Function

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

scholarly journals Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5888
Author(s):  
Joaquín Figueroa Barraza ◽  
Enrique López Droguett ◽  
Marcelo Ramos Martins
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Deep Learning ◽  
High Performance ◽  
Deep Neural Networks ◽  
Health Management ◽  
Remaining Useful Life ◽  
Health State ◽  
Prognostics And Health Management ◽  
Selection Framework

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.

scholarly journals Physics Based Deep Learning Technique for Prognostics

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Khaled Akkad
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Health Management ◽  
Recognition Performance ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Learning Technique ◽  
Useful Life

Remaining useful life (RUL) estimation is one of the most important aspects of prognostics and health management (PHM). Various deep learning (DL) based techniques have been developed and applied for the purposes of RUL estimation. One limitation of DL is the lack of physical interpretations as they are purely data driven models. Another limitation is the need for an exceedingly large amount of data to arrive at an acceptable pattern recognition performance for the purposes of RUL estimation. This research is aimed to overcome these limitations by developing physics based DL techniques for RUL prediction and validate the method with real run-to-failure datasets. The contribution of the research relies on creating hybrid DL based techniques as well as combining physics based approaches with DL techniques for effective RUL prediction.

scholarly journals Research on DC-DC power health management technology based on degradation test and deep learning

2021 ◽  
Vol 1207 (1) ◽  
pp. 012005
Author(s):  
Linlin Shi ◽  
Pengfei Yu ◽  
Shilie He ◽  
Zhenwei Zhou ◽  
Linghui Meng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Supply ◽  
Health Management ◽  
Fault Prediction ◽  
Health State ◽  
Current Output ◽  
Dc Power ◽  
Management Technology ◽  
Degradation Test ◽  
Dc Power Supply

Abstract The health state of DC-DC power supply is the key factor to determine whether the electronic equipment can operate normally. The failure and deterioration of the power supply system will lead to the collapse of the entire electronic system. The research in this paper is based on the long-term high temperature degradation test data of a certain type of DC-DC power supply. The degradation law of power supply is studied by data preprocessing and noise reduction of sensitive parameters such as input current, output current, input voltage and output voltage. On this basis, we use the method of deep learning to model the efficiency of power supply in the process of degradation test. The experimental results show that the efficiency time series modeling of power supply degradation using LSTM method can effectively reflect the law of power supply efficiency degradation. Based on the DC-DC power health management technology combining degradation test and deep learning, the advanced fault prediction model is used to reflect the change law of power supply in the real degradation process. This method has certain theoretical and engineering value for power PHM modeling and application.

scholarly journals Airborne Wireless Sensor Networks for Airplane Monitoring System

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shang Gao ◽  
Xuewu Dai ◽  
Yu Hang ◽  
Yuyan Guo ◽  
Qian Ji
Keyword(s):  
Monitoring System ◽  
Health Management ◽  
Low Cost ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Network Communication ◽  
Data Repository ◽  
Prognostics And Health Management ◽  
Cabin Environment ◽  
Central Data

In traditional airplane monitoring system (AMS), data sensed from strain, vibration, ultrasound of structures or temperature, and humidity in cabin environment are transmitted to central data repository via wires. However, drawbacks still exist in wired AMS such as expensive installation and maintenance, and complicated wired connections. In recent years, accumulating interest has been drawn to performing AMS via airborne wireless sensor network (AWSN) system with the advantages of flexibility, low cost, and easy deployment. In this review, we present an overview of AMS and AWSN and demonstrate the requirements of AWSN for AMS particularly. Furthermore, existing wireless hardware prototypes and network communication schemes of AWSN are investigated according to these requirements. This paper will improve the understanding of how the AWSN design under AMS acquires sensor data accurately and carries out network communication efficiently, providing insights into prognostics and health management (PHM) for AMS in future.

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