Suresh Perinpanayagam

The present and future of additive manufacturing in the aerospace sector: A review of important aspects

This paper reviews recent improvements in additive manufacturing technologies, focusing on those which have the potential to produce and repair metal parts for the aerospace industry. Electron beam melting, selective laser melting and other metal deposition processes, such as wire and arc additive manufacturing, are presently regarded as the best candidates to achieve this challenge. For this purpose, it is crucial that these technologies are well characterised and modelled to predict the resultant microstructure and mechanical properties of the part. This paper presents the state of the art in additive manufacturing and material modelling. While these processes present many advantages to the aerospace industry in comparison with traditional manufacturing processes, airworthiness and air transport safety must be guaranteed. The impact of this regulatory framework on the implementation of additive manufacturing for repair and production of parts for the aerospace industry is presented.

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.

Developing Prognostic Models Using Duality Principles for DC-to-DC Converters

Within the field of Integrated System Health Management, there is still a lack of technological approaches suitable for the creation of adequate prognostic model for large applications whereby a number of similar or even identical subsystems and components are used. Existing similarity among a number of different systems, which are comprised of similar components but with different topologies, can be employed to assign the prognostics of one system to other systems using an inference engine. In the process of developing prognostics, this approach will thereby save resources and time. This paper presents a radically novel approach for building prognostic models based on system similarity in cases where duality principle in electrical systems is utilized. In this regard, unified damage model is created based on standard Tee/Pi models, prognostics model based on transfer functions, and remaining useful life (RUL) estimator based on how energy relaxation time of system is changed due to degradation. An advantage is that the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of other systems. Simple electronic circuits, dc-to-dc converters, are to be used as an experiment to exemplify the potential success of the proposed technique validated with prognostics models from particle filter.

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery.

A Novel Intermittent Fault Detection Algorithm and Health Monitoring for Electronic Interconnections

There are various occurrences and root causes that result in no-fault-found (NFF) events but an intermittent fault (IF) is the most frustrating. This paper describes the challenging and most important area of an IF detection and health monitoring that focuses toward NFF situation in electronics interconnections. The experimental work focuses on mechanically-induced intermittent conditions in connectors. This paper illustrates a test regime, which can be used to repeatedly reproduce intermittence in electronic connectors, while subjected to vibration. A novel algorithm is used to detect an IF in interconnection. It sends a sine wave and decodes the received signal for intermittent information from the channel. This algorithm has been simulated to capture an IF signature using PSpice (electronic circuit simulation software). A simulated circuit is implemented for practical verification. However, measurements are presented using an oscilloscope. The results of this experiment provide an insight into the limitations of existing test equipment and requirements for future IF detection techniques. Aside from scheduled maintenance, this paper considers the possibility for in-service intermittent detection to be built into future systems, i.e., can IFs be captured without external test gear?

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.

Health monitoring of POL converter using digital PWM controller

In the aerospace industry, interest has been growing in monitoring the ongoing health of electronic products and systems to predict failures and provide early warnings for any catastrophic failures. Switch-mode power supplies form an integral part of the avionic line replaceable unit (LRU). Any failure in its operation will cause malfunctioning of the complete electronics and the system thereafter. Point-of-load (POL) converters in particular are non-isolated buck converters that provide low output voltage DC power close to the loads. In the last decade, the average LRU supplies evolved from a basic four output voltages to more than twenty output voltages, due to the increased number of low-voltage supply pins on Very Large Scale Integrated (VLSI) circuits. This increased number of POL converters mathematically increases power supply failure rates, hence the importance of being able to predict them. This paper presents a brief review of failures/degradation experienced by POL converters in avionic systems. Capabilities of currently used analog Pulse-Width Modulator (PWM) controllers and the digitally-controlled PWMs for health monitoring of electronic systems are addressed. Lastly, the technique of system identification for health monitoring of POL converter using Digital Pulse-Width Modulator (DPWM) circuits is proposed.

Aging Detection Capability for Switch-Mode Power Converters

The detection of degradations and resulting failures in electronic components/systems is of paramount importance for complex industrial applications including nuclear power reactors, aerospace, automotive, and space applications. There is an increasing acceptance of the importance of detection of failures and degradations in electronic components and of the prospect of system-level health monitoring to make a key contribution to detecting and predicting any impending failures. This paper describes a parametric system identification-based health-monitoring method for detecting aging degradations of passive components in switch-mode power converters (SMPCs). A nonparametric system response is identified by perturbing the system with an optimized multitone sinusoidal signal of the order of mVs. The parametric system model is estimated from nonparametric system response using recursive weighted least-square (WLS) algorithm. Finally, the power-stage component values, including their parasitics, are extracted from numerator and denominator coefficients based on the assumed Laplace system model. These extracted component values provide direct diagnostic information of any degradation or anomalies in the components and the system. A proof of concept is initially verified on a simple point-of-load (POL) converter but the same methodology can be applied to other topologies of SMPC.

Prognostic capability evaluation of power electronic modules in transportation electrification and vehicle systems

Health management and reliability are fundamental part of the design and development cycle of the power electronic products. This paper presents the study and investigation about the prognostic and health management of power electronic IGBT module. To achieve this aim, a fusion approach has been introduced. In addition, for the physic based part of approach, a thermal model is used to predict temperatures of inaccessible locations within the power module. Then by employing the rainflow algorithm, the remaining life of the power electronic IGBT module has been determined.

A simple state-based prognostic model for predicting remaining useful life of IGBT power module

Health management and reliability are fundamental aspects of the design and development cycle of power electronic products. This paper presents the prognostic evaluation of a power electronic IGBT module. To achieve this aim, a simple state-based prognostic (SSBP) method has been introduced and applied on the data which was extracted from an aged power electronic IGBT and its remaining useful life was determined.