Development of an expert system for the repair and maintenance of bulldozer's work equipment failure

This work aimed to develop an expert fault diagnostic system for the repair and maintenance of bulldozer work equipment faults. An ExpertSystem, ES is one of the many quick and efficient repairs and maintenance strategy that can be used on these machines. ES is a C# computer based programming software that can be adopted to extend the life span of equipments and reduce the cost of human expert for their repairs. In this work, an expert system was developed as a tool that will detect, analyse and proffer respective solutions to the bulldozer work equipment faults. A flowchart (logic chart) was also developed. The flowchart is a logical sequence for characterising and troubleshooting the causes of bulldozer’s work equipment failure. In this report, the solutions to the detected faults: low or high hydraulic valve pressure, abnormal noise in the control valve was documented accordingly. The preferred solutions to the various faults observed were also included with snapshots from each interface of the developed program in the report. The ES developed can be adopted in the construction industries for carrying out repair and maintenance of equipment for optimum performance at a highly reduced cost. This can also be used as a teaching aid in the department of mechanical and mechatronics engineering and other fields of engineering institute. This study will enable automobile and maintenance workshops to proffer solutions to maintenance of bulldozer’s work equipment failure and at the same time avoid costly damage and optimize the economic objective

Application of knowledge-based techniques to fault diagnosis of 16 QAM digital microwave radio equipment

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Experdite : a model based expert diagnostic system using a high level test description language

To be competitive in the development and production of complex electronic systems, a company must constantly scrutinize its manufacturing processes to find ways to reduce costs and increase productivity. One of the more costly problems is the inability to troubleshoot and repair faulty circuit boards on the spot. Rather, they must be divert.ed off the manufacturing line to a senior technician for diagnosis and repair. The purpose of Experdite is to provide a practical expert diagnostic system for manufacturing line personnel so they can quickly troubleshoot and repair most circuit board faults, thereby eliminating the need to send the board off line. Experdite also addresses other inherent manufacturing line issues, including the need to have a general purpose and easily adaptable system to work with many different circuit boards. It addresses the need for an expert diagnostician before anyone has bad the time to become an expert troubleshooter on the circuit board. The final issue addressed is the ability to quickly and easily modify the knowledge base to handle any inevitable hardware changes. The component and self-test behavior for the target circuit board is abstracted and then modeled with a high level Test Description Language, or TDL. A TDL to C++ translator converts the TDL description into a knowledge base of C++ classes. Then the inference engine traces the thread of causality to a fault on a circuit board by gleaning information from the knowledge base and comparing it to the actual behavior of the circuitry. This paper elaborates on each of these points. Also included in the paper are descriptions of the implementation and application of Experdite to a production circuit board

An Intelligent Failure Analysis System.

The investigation of commercial/industrial failures is a vital, but complex task. This paper presents an Intelligent Failure Analysis System (aIFAS). It is a system designed by a failure analyst with the goal of making failure investigation easier. The knowledge base for aIFAS comes from commercial laboratory reports. The methodologies employed represents the experience gained from over five years of development. One goal of aIFAS is to provide a case-based expert system tool to help find answers. Functionality ranges from matching a new case to stored example cases to extracting relational data from the aIFAS knowledge base. This study focuses on two objectives beyond implementation of aIFAS First, a more compact file structure to represent the failure mode/attribute data is explored. Second, five candidate metrics for case matching are compared. Comparisons are accomplished using a parametric analytic engine built into aIFAS. Combinations of features are tested against a single set of fifty cases, as well as, with multiple trials of randomly selected cases. The Relative Time Unit and Performance Score measures are introduced. They offer a semi-quantitative yardstick that introduces both accuracy and speed into the assessment process. A more compact, grouped format for attribute representation gave improved performance. It shows promise as a means to inject fuzzy logic into aIFAS. The City Block and Hamming distance algorithm were the most stable and efficient metrics