The following paper is a work in progress and is centered on the premise the Intelligent Transportation Systems (ITS), the self-driving automobile and the environment in which it operates will require the automobile to have an on-board Health and Usage Monitoring Systems (HUMS) to operate safely on roadways. HUMS are in their early stages of use to monitor vehicle components and critical structures to prevent routine and catastrophic failures. Vehicle health monitoring, prognostics, diagnostics and condition-based maintenance are the central tenants of a proposed HUMS for autonomous automobiles. Over the past decade, autonomous vehicles have been studied and developed for a real-world application. Upon identifying the critical assets, it is essential to create a design of experiment contingency plan to understand monitoring techniques required to conduct safe operations in ITS. This paper identifies the techniques required to monitor those components through a development of a design of experiments for the critical assets.
Keywords: Health and Usage Monitoring Systems, Prognostics, Autonomous Systems, Automobile
Due to the large-scale and complication modern engineering system and the introduction of high-tech, system reliability has become the key to control the development of complex systems. As a key basic technology in implementing system reliability engineering, reliability analysis technology is facing several technical difficulties and application challenges caused by complex systems. In the actual complex engineering system, the system often has a variety of complex relationships and dynamic characteristics, such as the order of failure of components, or Alternating work of the systems. At present, some achievements have been made in the analysis of fault tree considering dynamic failure characteristics. However, there is still a lack of research in considering Alternating work of the system, so that the results obtained by conventional methods are not in accordance with the actual situation or even far apart. The dynamic fault tree is a description model with powerful ability to describe dynamic systems. Therefore, this paper considers the establishment of a system reliability model based on dynamic fault tree. The dynamic fault tree includes the priority AND gate, the function related gate and the backup gate, which can describe part of the dynamic logic in the system, but can not describe alternating working of the logic of the system. Therefore, this article needs to expand the dynamic fault tree model, the method of extending the model is as follows: 1. Graphical description of the feature needs to be given based on graph theory and reliability theory; 2. Determine the input and output of the graph; 3. Give the definition of the graph and establish the logic gate that can describe the alternation of the system. After establishing the corresponding logic gates, the corresponding solution method of the established model is given. The solution method is modeled as follows: 1. According to the logic of this characteristic, the corresponding calculation flow chart is analyzed, and the flow chart of each step is transformed into the corresponding algorithm. For the specific problems involved, find the corresponding theoretical knowledge (such as conditional probability, total probability formula, Markov chain, etc.), and finally get the corresponding model solution method. After expanding the dynamic fault tree model, the reliability model of the whole system is established based on the extended dynamic fault tree. When solving, the dynamic fault tree is usually transformed into Markov model. In this paper, the dynamic fault tree is first modularized to obtain independent static subtree and dynamic subtree, which are respectively solved by binary decision diagram method and Markov process method. Take a production system for example, using this method calculate the reliability of the production system.
