An Intelligent Planning-Based Modeling Method for Diagnosis and Repair

Chuang Li,Wei Wei,Dantong Ouyang,Xiaoyu Wang

doi:10.1155/2020/8887933

Abstract

Planning and model-based diagnosis are both branches of artificial intelligence. In model-based diagnosis, multiresults may be gotten which lead to an uncertain diagnosis. We use the landmark method from planning to designing an event sequence to get a reaction. A method that uses planning to repair in local results of incremental diagnosis is proposed. Firstly, a model is established on model-based diagnosis and planning. Incremental diagnosis results are used as the initial state of planning, and the heuristic search method is used to find the solution to an unfaulty state. Two algorithms with different strategies are designed for diagnosis and repair: one is to repair all possible faults and use controllable events to repair them, and the other is to test through the feedback of controllable events and observable events to get the only solution and repair them. At the same time, the efficiency of the incremental diagnosis method is improved based on heuristics.

Highlights

AI planning and model-based diagnosis are developing at the forefront of AI research
The model-based diagnosis method was originally used in static systems [2]; the conflict-based method was used in other problems such as knowledge representing [3] and planning [4]
The frameworks of three tanks were used in the data set, but the behavior set was expanded, and the controllable event set was added

Summary

Introduction

AI planning and model-based diagnosis are developing at the forefront of AI research. Diagnostic problems originate from medicine and extend to the detection of equipment failure or behavior deviation. Model-based diagnosis is a diagnostic method of common reasoning based on the equipment model and behavior. The model-based diagnosis method was originally used in static systems [2]; the conflict-based method was used in other problems such as knowledge representing [3] and planning [4]. As the scale of equipment increases, the behavior becomes more and more complex and the loss caused by shutdown increases. The dynamic diagnosis that can be monitored online is put forward [5, 6] and has been extended to various industries

Methods

Results

Conclusion