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1.发明授权Detecting anomalies in fault code settings and enhancing service documents using analytical symptoms 有权检测故障代码设置中的异常，并使用分析症状来增强服务文档公开(公告)号：US08509985B2
公开(公告)日：2013-08-13
申请号：US13115216
申请日：2011-05-25
IPC分类号： G06F17/00
CPC分类号： G05B23/0278
摘要： A method is provided for identifying a root cause of a fault in a serviced vehicle based on analytical symptoms. Parameter identification data associated with identified DTCs is retrieved. Parameter identification data from a plurality of vehicles experiencing the DTCs is collected. A first set of diagnostic rules is generated that identify vehicle operating parameters for executing a DTC algorithm or for triggering a DTC. A second set of diagnostic rules is generated that identify vehicle operating parameters used for selecting field failure data obtained when the DTC is triggered. Statistically significant rules are extracted from the second set of diagnostic rules. The first set of rules and the statistically significant rules are cooperatively applied to the parameter identification data for identifying a subset of the parameter identification data that represents anomalies. A subject matter expert analyzes the anomalies for identifying a root cause of the fault.
摘要翻译： 提供了一种基于分析症状来识别服务车辆中的故障的根本原因的方法。 检索与识别的DTC相关联的参数识别数据。 收集来自经历DTC的多个车辆的参数识别数据。 生成第一组诊断规则，识别用于执行DTC算法或触发DTC的车辆操作参数。 产生第二组诊断规则，其识别用于选择触发DTC时获得的现场故障数据的车辆操作参数。 从第二组诊断规则中提取统计学上重要的规则。 协调地将第一组规则和统计上有意义的规则应用于参数识别数据，以识别代表异常的参数识别数据的子集。 主题专家分析了确定故障根本原因的异常情况。
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2.发明申请DETECTING ANOMALIES IN FAULT CODE SETTINGS AND ENHANCING SERVICE DOCUMENTS USING ANALYTICAL SYMPTOMS 有权使用分析性症状检测故障代码设置中的异常和增强服务文档公开(公告)号：US20120303205A1
公开(公告)日：2012-11-29
申请号：US13115216
申请日：2011-05-25
IPC分类号： G06F17/00
CPC分类号： G05B23/0278
摘要： A method is provided for identifying a root cause of a fault in a serviced vehicle based on analytical symptoms. Parameter identification data associated with identified DTCs is retrieved. Parameter identification data from a plurality of vehicles experiencing the DTCs is collected. A first set of diagnostic rules is generated that identify vehicle operating parameters for executing a DTC algorithm or for triggering a DTC. A second set of diagnostic rules is generated that identify vehicle operating parameters used for selecting field failure data obtained when the DTC is triggered. Statistically significant rules are extracted from the second set of diagnostic rules. The first set of rules and the statistically significant rules are cooperatively applied to the parameter identification data for identifying a subset of the parameter identification data that represents anomalies. A subject matter expert analyzes the anomalies for identifying a root cause of the fault.
摘要翻译： 提供了一种基于分析症状来识别服务车辆中的故障的根本原因的方法。 检索与识别的DTC相关联的参数识别数据。 收集来自经历DTC的多个车辆的参数识别数据。 生成第一组诊断规则，识别用于执行DTC算法或触发DTC的车辆操作参数。 产生第二组诊断规则，其识别用于选择触发DTC时获得的现场故障数据的车辆操作参数。 从第二组诊断规则中提取统计学上重要的规则。 协调地将第一组规则和统计上有意义的规则应用于参数识别数据，以识别代表异常的参数识别数据的子集。 主题专家分析了确定故障根本原因的异常情况。
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