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.

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 PHM Survey : Implementation of Diagnostic Methods for Monitoring Industrial Systems

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Abdenour Soualhi ◽  
Bilal Elyousfi ◽  
Yasmine Hawwari ◽  
Kamal Medjaher ◽  
Guy Clerc ◽  
...  
Keyword(s):  
Life Cycle ◽  
Health Management ◽  
Fault Isolation ◽  
Diagnostic Methods ◽  
Condition Based Maintenance ◽  
Prognostics And Health Management ◽  
Isolation And Identification ◽  
Industrial Systems ◽  
Industrial Sectors ◽  
Processing Step

The modernization of industrial sectors involves the use of complex industrial systems and therefore requires condition based maintenance. This one aims at increasing the operational availability and reducing the life-cycle while increasing the reliability and life expectancy of industrial systems. This maintenance also called predictive maintenance is a part of an emerging philosophy called PHM ‘Prognostics and Health Management’. In this paper, the PHM will be emphasized on the existing diagnostic methods used for fault isolation and identification. This depicts an important part of the PHM as it exploits the data given by the signal-processing step and its output is treated by the prognostic part. The diagnostic is mainly classified in three categories that will be highlighted in this paper.

Identifying Parameters for Nonlinear Degradation-Based Prognostics

2019 ◽  
pp. 237-260
Author(s):  
Feng Yang ◽  
Mohamed Salahuddin
Keyword(s):  
Parameter Identification ◽  
Induction Motors ◽  
Health Management ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Prognostics And Health Management ◽  
Health Index ◽  
Identification Method ◽  
Useful Life ◽  
Parameter Values

Prognostics and health management (PHM) methodologies are increasingly playing active roles in improving the availability, reliability, efficiency, productivity, and safety of systems in many industries. In predicting the remaining useful life (RUL), this chapter introduces a prognostics framework with health index (HI) formulation, with specific emphasis on incorporating and validating nonlinear HI degradations. The key issue to the success of this framework is how to identify appropriate parameters in describing the behavior of the nonlinear HI degradations. Using exponential HI degradation as an example in predicting the RULs of induction motors, this chapter discusses three different explorations in verifying the existence of good parameter values as well as identifying the appropriate parameters automatically. Comprehensive experiments were carried out with degradation process (DP) data from eight induction motors, and it was discovered that good parameters can be automatically determined with the proposed parameter identification method.

scholarly journals An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data

2021 ◽  
Vol 11 (11) ◽  
pp. 5180
Author(s):  
Donghwan Kim ◽  
Seungchul Lee ◽  
Daeyoung Kim
Keyword(s):  
Health Management ◽  
Real Life ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Prognostics And Health Management ◽  
Health Index ◽  
Computing Technology ◽  
Simulation Data ◽  
Failure Data ◽  
Useful Life

As technology advances, the equipment becomes more complicated, and the importance of the Prognostics and Health Management (PHM) to monitor the condition of the equipment has risen. In recent years, various methodologies have emerged. With the development of computing technology, methodologies using machine learning and deep learning are gaining attention, in particular. As these algorithms become more advanced, the performance of detecting anomalies and predicting failures has improved dramatically. However, most of the studies are cases that depend on simulation data or assumed abnormal conditions. In addition, regardless of the existence of run-to-failure data, the methodologies are difficult to apply to the industrial site directly. To solve this problem, we propose a Predictive Maintenance (PdM) framework based on unsupervised learning in this paper, which can be applied directly in the industrial field regardless of run-to-failure data. The proposed framework consists of data acquisition, preprocessing data, constructing a Health Index, and predicting the remaining useful life. We propose a framework that can create and monitor models even when there are no accumulated run-to-failure data. The proposed framework was conducted in two different real-life cases, and the usefulness and applicability of the proposed methodology were verified.

Prognostics and Health Management of Aircraft Engine EMA Systems

2011 ◽  
Author(s):  
Matthew J. Watson ◽  
Matthew J. Smith ◽  
Jared Kloda ◽  
Carl S. Byington ◽  
Kenneth Semega
Keyword(s):  
Health Management ◽  
Aircraft Engine ◽  
Life Cycles ◽  
Remaining Useful Life ◽  
System Level ◽  
System Response ◽  
Prognostics And Health Management ◽  
Ideal System ◽  
Starting Point ◽  
Operational Life

In this study, the authors conducted a model-based, engine system analysis of Electro-Mechanical Actuators (EMAs). This effort employed an existing, NASA developed, aircraft engine model. A critical engine actuator within the model was replaced by a dynamic, physics based EMA model that includes: controller, motor, drivetrain and feedback sensor sub-models. The actuator model includes simulation of the electrical, mechanical and thermal response of the system. The resulting platform was used to simulate a range of critical actuator fault conditions including: feedback resolver fault, ball-screw degradation, motor winding short, and LVDT non-linearity. Since the available experimental data from propulsion system EMAs is very limited, this platform provides an ideal opportunity to evaluate and enhance prognostic capability for critical engine applications. The model fault tests were used to demonstrate a prototype prognostics and health management (PHM) system for engine EMAs. First, the system response was used to develop an appropriate mode detection algorithm to identify the ideal system conditions for collection of diagnostic evidence. Then, using the acquired transient and steady-state system response, diagnostic data features were derived from EMA related sensors and engine performance parameters. Using these features as a starting point, a system level reasoner was created using multiple classification techniques including LDA, QDA and SVM. Using model generated data with simulated system variance, it was demonstrated that the reasoner provides excellent fault detection, isolation and severity assessment capability for all considered fault modes. Finally, a suitable actuator life model was developed and a probabilistic prognostic approach was used to determine the remaining useful life of the system. The demonstrated PHM system will significantly enhance the ability to safely operate aircraft, schedule maintenance activities, optimize operational life cycles, and reduce support costs.

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