Face haulage equipment failure analysis. Volume II. Computer sort results and user's manual. Final technical report as of November 30, 1980. [No text; Data and user's manual only]

Purpose The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships. Design/methodology/approach This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor. Findings Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain. Originality/value It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

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Developing an Expert System of Failure Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.2375 ◽

2013 ◽

Vol 284-287 ◽

pp. 2375-2379

Author(s):

Yeong Ho Ho ◽

Huei Sen Wang ◽

Hei Chia Wang

Keyword(s):

Expert System ◽

Failure Analysis ◽

Major Elements ◽

Case Based Reasoning ◽

Test Results ◽

Inference Method ◽

Ranking List ◽

Document Ranking ◽

Equipment Failure Analysis ◽

Inference Methods

An Equipment Failure Analysis Expert System (EFAES) is to be developed to help the engineers diagnose the causes of the failure mechanism and provide a reliable remedy. This system is based on an innovative reasoning approach: integrating the rule-based reasoning (RBR) and the case-based reasoning (CBR) methods The architecture developed in the system consists of six major elements-“Factor and Attribute Editor”, Knowledge Actuation Interface”, “Knowledge Base”, “User Interface”, “Inference Engine” and “Explanation Facility”. Here, the RBR system consists of 46 failure mechanisms and their rules. The CBR system consists of 586 failure cases which are coded and composed from 23 factors and their 265 attributes. Also, this system provides a variety of inference methods which allows retrieving the best answers to users. For the RBR system, performance is directly check the inferred order of the document ranking list. For the CBR system, the effectiveness of each inference method is evaluated by using “Recall”, “Precision”, and “F-Measure” approaches. From the test results, many recommendations are proposed.

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Failure Analysis With the Scanning Electron Microscope

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100095662 ◽

1972 ◽

Vol 30 ◽

pp. 482-483

Author(s):

John R. Devaney

Keyword(s):

Electron Microscope ◽

Scanning Electron Microscope ◽

Integrated Circuits ◽

Failure Analysis ◽

High Reliability ◽

Military Applications ◽

Small Structure ◽

Useful Life ◽

Insight Into ◽

Scanning Electron

Occasionally in history, an event may occur which has a profound influence on a technology. Such an event occurred when the scanning electron microscope became commercially available to industry in the mid 60's. Semiconductors were being increasingly used in high-reliability space and military applications both because of their small volume but, also, because of their inherent reliability. However, they did fail, both early in life and sometimes in middle or old age. Why they failed and how to prevent failure or prolong “useful life” was a worry which resulted in a blossoming of sophisticated failure analysis laboratories across the country. By 1966, the ability to build small structure integrated circuits was forging well ahead of techniques available to dissect and analyze these same failures. The arrival of the scanning electron microscope gave these analysts a new insight into failure mechanisms.

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