A Soft Computing Approach for Fault Prediction of Electronic Systems Ajith Abraham & Baikunth Nath School of Computing & Information Technology Monash University (Gippsland Campus), Victoria 3842, Australia Email: Ajith.Abraham@infotech.monash.edu.au Abstract This paper presents a soft computing approach for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the stressor – susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For on- line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after preprocessing and standardization is fed into the neuro-fuzzy model. For comparison purpose, we also trained a artificial neural network using backpropagation learning and evaluated the comparative performance. The performance of the proposed method of prediction is evaluated by comparing the experimental results with the actual failure model values. Test results reveal that neuro-fuzzy models outperformed neural network in terms of performance time and error achieved. Keywords: Soft computing, hybrid system, neuro-fuzzy, artificial neural network, stressor, susceptibility, Monte Carlo analysis, failure prediction. 1. Introduction Real time monitoring of the healthiness of complex electronic systems/ circuits is a difficult challenge to both human operators and expert systems. When the electronic circuit or system is controlling a critical task fault prediction will be very important. Soft computing was first proposed by Zadeh [14] to construct new generation computationally intelligent hybrid systems consisting of neural networks, fuzzy inference system, approximate reasoning and derivative free optimization techniques. It is well known that the intelligent systems, which can provide human like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time varying environment, are important in tackling practical computing problems. In contrast with conventional Artificial Intelligence (AI) techniques which only deal with precision, certainty and rigor the guiding principle of hybrid systems is to exploit the tolerance for imprecision, uncertainty, low solution cost, robustness, partial truth to achieve tractability, and better rapport with reality. The new technique of electronic system failure prediction using stressor- susceptibility interaction is briefly discussed in Section 2 [1][2], [5]. Section 3 presents practical methods for obtaining stressor sets. The derivation of stressor sets using Monte Carlo Analysis is given in Section 4 followed by Section 5 wherein we had derived a stressor-susceptibility model for a circuit. Section 6 and 7 gives some theoretical background about neuro-fuzzy systems and artificial neural networks. In section 8 we have reported the experimentation results and finally conclusions are provided in Section 9. 2. Stressor-Susceptibility Interaction Stressor is a physical entity influencing the lifetime of a component or circuit. A stressor, indicating a physical entity x will be denoted as x ψ . Stressors can be broadly classified into three main groups. First group contains the electrical stressors, parameters related to the electrical behavior of the circuit. Second group of stressors is the mechanical stressors, which are related to the mechanical environment of the component. Third group of parameters influencing the lifetime of components is related to the thermal environment of the component. Susceptibility of a component to a certain failure mechanism is defined as the probability function indicating the probability that a component will not remain operational for a certain time under a given combination of stressors. The susceptibility related to the failure mechanism “y” is usually defined as S y (t, ψ p , ψ q ,ψ r ). Failure probabilities require detailed analysis of both stressors and susceptibility. Most components tend to have more than one failure mechanism, resulting in more than one “failure probability”. It can be shown that there is a strong correlation between the various failure mechanisms existing within a component. Figure 1. Stressor-Susceptibility interaction for single failure mechanism. Figure 1 illustrates the stressor - susceptibility interaction for a single failure mechanism. It is clear that the main source of problem is the overlap between stressor and susceptibility density. The first step is to calculate the failure probability for this stressor distribution on a failure mechanism with a single, one variable, time independent catastrophic susceptibility model. This results in the following probability ( ) ( ) Ψ Ψ ∞ Ψ = Ψ Ψ ò d y f y fail f 0 0 , , To calculate the failure probability as a function of more complex susceptibility model, it will be necessary to calculate the failure probability of a part of the susceptibility model, for a certain stressor interval Δ, characterized by its mean value ψ o and the corresponding susceptibility density function at that point S y (ψ o ). Considering the probability that a part has failed at a lower susceptibility level, result in the possibility to predict the failure probability per time interval of a certain failure at stressor level ψ 0 using the following relation ( ) ÷ ÷ ÷ ø ö ç ç ç è æ Δ - - Ψ Ψ ∞ Ψ Ψ Δ = Ψ Ψ ò ò 0 0 ) ( , 1 ) ) ( 0 ) 0 ( ( 0 , , ψ ψ ψ d y fail f d y f Y S y fail f The last term is introduced to subtract failures caused by stressors at a lower susceptibility level. As, most often, failure probabilities are very small, in many cases the previous expression will simplify to ) ) ( ) ( ( ) ( 0 0 0 , , Ψ Ψ Ψ Δ = ò ∞ Ψ d f S f y y y fail ψ ψ