T. Sreenuch

Computationally Efficient, Real-Time, and Embeddable Prognostic Techniques for Power Electronics

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.

Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life

Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated.

Distributed embedded condition monitoring systems based on OSA-CBM standard

This paper presents an approach to distributed condition monitoring systems that offers reusable software for a class of condition monitoring (CM) applications. The focus is on an open software framework for development of CM applications stemming from 1) OSA-CBM specification, which is a standard promoting interoperability, and 2) a component framework that enables reuse, configuration and rapid deployment. The framework is developed using Java and RMI middleware, and its application is demonstrated through a distributed gearbox CM system. The approach provides software enabled capability to distribute the CM data process across the hardware platforms to meet the given system configuration.

Multi-Domain Modeling and Simulation of an Aircraft System for Advanced Vehicle-Level Reasoning Research and Development

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.

Fuzzy gain-scheduled missile autopilot design using evolutionary algorithms

The paper presents the lateral acceleration control design of a missile model using the evolution strategy. The non-linear fixed-structure-like controller is represented by the singleton fuzzy model. This allows a variation of shapes of the gain functions to be explored, and subsequent modification is fairly easy. Instead of finding an optimal decomposition of the fuzzy controller, a set of fuzzy rule is unconventionally optimized for the overall gain surfaces that guarantee acceptable performances for the closed-loop system over the whole operating envelope. The simulation results show that the designed fuzzy gain-scheduled controller is a robust tracking controller for all perturbation vertices.

Lateral Acceleration Control Design of a Non-Linear Homing Missile

Abstract This paper presents the lateral acceleration control design of non-linear missile model using the multiple single objective Pareto sampling method. The LTI controller design for the uncertain plants is carried out by minimising gain-phase margin and tracking frequency domain based performance objectives. The Pareto optimal solutions (corresponding to a given set of weight vectors) are obtained. The selected solution, as illustration, is analysed. The gain-scheduling controller is obtained by the interpolation of zeros, poles and gains, where the smooth deterministic transtion rule is implemented using the TS fuzzy model. The non-linear simulation results show that the selected interpolated controller is a robust tracking controller for all perturbation vertices.

Particle Filter with Operational-Scalable Takagi-Sugeno Fuzzy Degradation Model for Filter-Clogging Prognosis

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 real-time 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 ...

Software Framework for Prototyping Embedded Integrated Vehicle Health Management Applications

Integrated vehicle health management is a major component in a new, future fleet-management paradigm where a conscious effort is made to shift aircraft maintenance from a schedule-based approach to a more proactive and predictive approach (that is, condition-based maintenance). Its goal is to maximize fleet operational availability while minimizing the logistics footprint through monitoring of the deterioration of equipment conditions. A comprehensive integrated vehicle health-management system will be executed in an environment that includes different sensor technologies, multiple information systems, and different data models. Integrated vehicle healthmanagement implementers have to therefore deal with an integration problem that involves different specialized algorithms and embedded hardware platforms. Integrated vehicle health management applications will have common execution logic, and many will share the same data-processing algorithms; hence, development productivity and the quality of integrated ...

Condition Based Maintenance Optimization of an Aircraft Assembly Process Considering Multiple Objectives

The Commercial Aircraft Cooperation of China (COMAC) ARJ21 fuselage’s final assembly process is used as a case study. The focus of this paper is on the condition based maintenance regime for the (semi-) automatic assembly machines and how they impact the throughput of the fuselage assembly process. The fuselage assembly process is modeled and analyzed by using agent based simulation in this paper. The agent approach allows complex process interactions of assembly, equipment, and maintenance to be captured and empirically studied. In this paper, the built network is modeled as the sequence of activities in each stage, which are parameterized by activity lead time and equipment used. A scatter search is used to find multiobjective optimal solutions for the CBM regime, where the maintenance related cost and production rate are the optimization objectives. In this paper, in order to ease computation intensity caused by running multiple simulations during the optimization and to simplify a multiobjective formulation, multiple Min-Max weightings are used to trace Pareto front. The empirical analysis reviews the trade-offs between the production rate and maintenance cost and how sensitive the design solution is to the uncertainties.

Lateral acceleration control design of a non-linear missile: a soft computing approach

This paper presents the lateral acceleration control design of non-linear missile model using the multiple single objective Pareto sampling method. The LTI controller design for the uncertain plants is carried out by minimizing gain-phase margin and tracking frequency domain-based performance objectives. The Pareto optimal solutions (corresponding to a given set of weight vectors) are obtained. The selected solution, as illustration, is analysed. The gain-scheduling controller is obtained by the interpolation of zeros, poles and gains, where the smooth deterministic transition rule is implemented using the TS fuzzy model. The non-linear simulation results show that the selected interpolated controller is a robust tracking controller for all perturbation vertices.