Symptombaserad felsökning av tunga fordon : En systematisk metod för att sammankoppla kundsymptom med systemreaktioner
Sammanfattning: This thesis is about symptom-based troubleshooting of heavy vehicles. The existing troubleshooting system at Scania is adapted to handle errors based on electronic fault codes. This means that some faults, such as mechanical faults when sensors are missing, are difficult to troubleshoot. In the thesis, a method is developed that will be a part of a symptom-based troubleshooting system which can handle all types of errors. The main objectives of the thesis are both to develop a method that can link customer symptoms with system reactions and also to develop formats for both customer symptoms and FMEA for the developed method. In the thesis, a literature study was first conducted in which troubleshooting methods and principles for the formalization of customer data were identified. The identified troubleshooting methods were Bayesian Network, Case-Based-Reasoning and Fault tree analysis. A case study was then conducted which was based on several documents for troubleshooting in gas engines and gas tanks. In the case study, data from the literature study and the empirically collected data were used to develop the final concept of the method. The case study included, among other things, semi-structured interviews to map out the existing troubleshooting process, and a workshop to choose the final concept. In order to meet the objectives of the thesis two research questions and one question linked to the case study were formulated: Research Questions: • RQ1: How is the troubleshooting process affected by the methods that can be used to link customer symptoms with system reactions in heavy vehicles? • RQ2: How can customer data and FMEA be formalized in order to be useful in the troubleshooting process of heavy vehicles? Case Study: • What kind of data is missing from Scania’s existing documentation to link customer symptoms with system reactions? The thesis resulted in a method based on two troubleshooting methods Bayesian network and Case-Based-Reasoning. The method links customer symptoms with system reactions by excluding human considerations and instead relying on previously documented cases and probabilities. A requirement for using this method is a cooperation between customer support, mechanics and development engineers. The formalization of customer symptoms in the developed method is based on what good data is for mechanics in troubleshooting contexts and what customers are capable of communicating; deviation – the customer’s description of the vehicle’s unexpected condition, position – where the customer considers the deviation to be present, context – what happened before, during and after the deviation was discovered. The conclusions that can be drawn is that it is not necessary to link customer symptoms with system reactions since the developed method allows the customer symptoms to be linked directly to the corrective actions needed. In addition, it was noted that the existing documentation at Scania on customer symptoms and system reactions is insufficient. However, this is not problematic as it was shown that FMEA is redundant for the method developed. In order for customer data to be useful, the formalization should include deviation, position and context. Further conclusions are that the role of the customer support becomes less critical when data driven troubleshooting methods are used, and that the accuracy of the developed method will improve over time as more data will be collected.
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