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Although increased machine automation is crucial for higher productivity, it also leads to higher susceptibility of component failures. Such failures may be caused by a variety of issues. This paper focuses on a technique that can be embedded in the software module that the particular component is interfaced with, to arrive at more specific information for determination of unanticipated usage modes causing component fatigue and consequent failure. Thus this may be used to guide component redesign if warranted. The paper proposes the use of finite state machines (FSMs) to implement software used to read the components. The FSM method used as a software architectural construct in conjunction with Markov chains can determine the limiting state distribution as a probability vector. This gives the maximum proportion of time the failed component would end up being in a particular state, along with information about source state and destination state from the Markov transition matrix.

A modern vehicle consists of multiple controllers that communicate with each other on a network to orchestrate the various operations of the vehicle in a coordinated manner. Most often, such networks are based on CAN (Controller Area Network). CAN was primarily implemented to allow for exchange of information on the network to accomplish optimal vehicle functionality. However this information need not be confined to data required by other controllers on the network. This information may also be used to expose the internal functioning of the controller and may be targeted to a special debug node that can extract and decipher this information. Such information can be evaluated to vastly augment debug capabilities while a particular controller is installed on the vehicle. This paper chronicles the authors' experience in developing such a methodology based on the J1939 CAN protocol, and illustrates its use using real world examples.