Power electronics are increasingly important in new generation vehicles as critical safety mechanical subsystems are being replaced with more electronic components. Hence, it is vital that the health of these power electronic components is monitored for safety and reliability on a platform. The aim of this paper is to develop a prognostic approach for predicting the remaining useful life of power electronic components. The developed algorithms must also be embeddable and computationally efficient to support on-board real-time decision making. Current state-of-the-art prognostic algorithms, notably those based on Markov models, are computationally intensive and not applicable to real-time embedded applications. In this paper, an isolated-gate bipolar transistor (IGBT) is used as a case study for prognostic development. The proposed approach is developed by analyzing failure mechanisms and statistics of IGBT degradation data obtained from an accelerated aging experiment. The approach explores various probability distributions for modeling discrete degradation profiles of the IGBT component. This allows the stochastic degradation model to be efficiently simulated, in this particular example ∼1000 times more efficiently than Markov approaches. Index Terms-Isolated-gate bipolar transistor (IGBT), Monte-Carlo simulation (MCS), power electronics, prognostics, remaining useful life (RUL).
This paper presents an approach to distributed condition monitoring systems that offers a reusable software architecture for a class of condition monitoring (CM) applications. The focus of this paper deals with an open software framework for development of CM applications stemming from 1) the Open System Architecture for Condition Based Maintenance (OSA-CBM) specification, which is an architecture promoting interoperability, and 2) a component framework that enables reuse, data process partitioning, configuration and rapid deployment. The publish/subscribe mechanism is the primary model used for both intra-and inter-module communications. The framework is developed using Java and Remote Method Invocation (RMI) distributed middleware, and its application is demonstrated through a gearbox CM system, where the CM software are deployed on the distributed embedded devices. This approach provides software enabled capability to distribute/reconfigure the CM data process (through the OSA-CBM common interface and data model) across the hardware platforms to meet the given system configuration.
In this paper, filter clogging is used as an aerospace integrated vehicle health management case study to demonstrate the proposed prognostic approach. The focus of this paper is on a scalable data-driven degradation model and how it can improve the remaining useful life prediction performance in condition monitoring of a filter component. Instead of overall fitting of the data, a degradation pattern (a parameterized Takagi-Sugeno fuzzy model) is learned from experimental data collected under a range of operating conditions in the proposed approach. The parameter allows the model to scale to fit different degradation profiles, and hence a more accurate model. In realtime condition monitoring, the degradation and model parameter are simultaneously estimated online based on noisy measurement updates using a particle filter. The estimation results show close tracking of the degradation state and good convergence of the model parameter to its real value. The remaining useful life prediction results show low prediction errors, regardless of operating conditions, which contrasts to a conventional data-driven model (a nonparameterized Takagi-Sugeno fuzzy model) where prediction errors increase as operating conditions deviate from the nominal condition. I. IntroductionI NTEGRATED vehicle health management (IVHM) is a major component in a new future aerospace asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled-based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimizing downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing, which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics, and providing recommended maintenance actions.The fuel system is a critical subsystem of the mobile platform asset type, like aircraft, where its functional failures can lead to an aircraft returning to ground or diverting and a remote possibility of engine shutdown. In an economic term, this means a significant cost to an operator. Several IVHM-related examples of fuel systems are reported in the literature: notably, those based on classification and estimation techniques [1], the applied support vector machine, k-nearest neighbors, and the Bayes classifier in fault detection and isolation (FDI) of an experimental electronic control fuel system. In [2,3], a rule-based expert system is developed for FDI of an aircraft fuel system. The Kalman filter is different, as it is an online parameter estimation technique and has been applied to the FDI of an aircraft fuel system [4] and a diesel engine [5]. The previously mentioned literature, however, only addresses FDI problems. It does not predict deterioration of a system, which is key to predictive maintenance.Until now, [6] was the only study related to prognostics of the fuel system; however, the results show s...
Abstract-In this paper, we describe a simulation based health monitoring system test-bed for aircraft systems. The purpose of the test-bed is to provide a technology neutral basis for implementing and evaluation of reasoning systems on vehicle level and software architecture in support of the safety and maintenance process. This simulation test-bed will provide the sub-system level results and data which can be fed to the VLRS to generate vehicle level reasoning to achieve broader level diagnoses. This paper describes real-time system architecture and concept of operations for the aircraft major sub-systems. The four main components in the real-time test-bed are the aircraft sub-systems (e.g. battery, fuel, engine, generator, heating and lighting system) simulation model, fault insertion unit, health monitoring data processing and user interface. In this paper, we adopted a component based modelling paradigm for the implementation of the virtual aircraft systems. All of the fault injections are currently implemented via software. The fault insertion unit allows for the repeatable injection of faults into the system. The simulation test-bed has been tested with many different faults which were undetected on system level to process and detect on the vehicle level reasoning. This article also shows how one system fault can affect the overall health of the vehicle.
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