The automotive industry designs, manufactures and maintains systems in which malfunctions or defects can be detected at different stages of a vehicle's lifecycle, e.g., the R&D process, manufacturing, or operations. Warnings of possible defects or malfunctions are based on test results as well as on-board and off-board signals. Effective diagnosis systems can help the driver in a single incident, and the vehicle manufacturer in the longterm. In all cases, data need to be gathered and communicated, and measures need to be taken on a timely basis to avoid expensive recalls, accidents, and/or commercial failures. Risk analysis models based on engineering systems analysis, probability, and human and management factors, can support the design of efficient warning systems. The authors present here some characteristics of effective signals, lessons learned from past failures in different industries, and analytical approaches to the design of monitoring systems that can beneficial in the automotive industry. The most useful signals are clear (unambiguous), accurate, specific, actionable, and received with sufficient lead-time for an effective response. Therefore, the most valuable sensors are those that provide such signals with minimal numbers of false positives and false negatives, along with an indication of the criticality of the message and of the lead time for action. Good diagnoses are based on the prior probabilities (e.g., mean future frequencies) of problems of various levels of severity, on the quality and timeliness of the signals, and on the effectiveness of corrective actions. Existing mathematical tools can provide decision support in the design of diagnosis systems in the automotive industry, just as they do in the fields of medicine or space exploration.
Risk analysis for monitoring and diagnosis of problems in the automotive industry
Risikoanalysemodelle für ein effizientes Problem-Frühwarnsystem für die Automobilindustrie
2004
11 Seiten, 4 Bilder, 16 Quellen
Conference paper
English
Risk Analysis for Monitoring and Diagnosis of Problems in the Automotive Industry
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