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    • 8. 发明申请
    • EVENT-DRIVEN FAULT DIAGNOSIS FRAMEWORK FOR AUTOMOTIVE SYSTEMS
    • 用于汽车系统的事件驱动故障诊断框架
    • US20110238258A1
    • 2011-09-29
    • US12730883
    • 2010-03-24
    • Satnam SinghRahul ChougulePulak Bandyopadhyay
    • Satnam SinghRahul ChougulePulak Bandyopadhyay
    • G06F7/00G01M17/00G06F17/10G06N3/02
    • G07C5/0808
    • Systems and methods for capturing and analyzing significant parameter data from vehicle systems whenever a diagnostic trouble code (DTC) is triggered. A multi-dimensional matrix is constructed, with vehicles, DTCs, and parameter data comprising three dimensions of the matrix. The data matrix is populated with DTC and parameter data from many different vehicles, either when vehicles are taken to a dealer for service, or via wireless data download. Time can be added as a fourth dimension of the matrix, providing an indication of whether a particular system or component is temporally degrading. When sufficient data is accumulated, the data matrix is pre-processed, features are extracted from the data, and the features are classified, using a variety of mathematical techniques. Trained classifiers are then used to diagnose the root cause of any particular fault signal, and also to provide a prognosis of system health and remaining useful life.
    • 每当触发诊断故障代码(DTC)时,从车辆系统捕获和分析重要参数数据的系统和方法。 构建多维矩阵,其中车辆,DTC和包括矩阵三维的参数数据。 当车辆被送往经销商进行维修或通过无线数据下载时,数据矩阵中填充有来自许多不同车辆的DTC和参数数据。 时间可以作为矩阵的第四维度添加,提供特定系统或组件是否在时间上有所降低的指示。 当累积足够的数据时,数据矩阵被预处理,从数据中提取特征,并且使用各种数学技术对特征进行分类。 然后训练分类器用于诊断任何特定故障信号的根本原因,并且还提供系统健康和剩余使用寿命的预后。
    • 9. 发明申请
    • DETECTING ANOMALIES IN FIELD FAILURE DATA
    • 检测现场故障数据中的异常
    • US20110137711A1
    • 2011-06-09
    • US12630866
    • 2009-12-04
    • Satnam SinghPulak BandyopadhyayCalvin E. Wolf
    • Satnam SinghPulak BandyopadhyayCalvin E. Wolf
    • G06Q10/00G06F15/00
    • G06Q10/06G06F11/079G06Q10/0639G06Q10/20G07C5/0808
    • A method of detecting anomalies in the service repairs data of equipment. A failure mode-symptom correlation matrix correlates failure modes to symptoms. Diagnostic trouble codes are collected for an actual repair for the equipment. The diagnostic trouble codes are provided to a diagnostic reasoner for identifying failure modes. Diagnostic assessment is applied by the diagnostic reasoner for determining the recommended repairs to perform on the equipment in response to identifying the failure modes. Each of the recommended repairs is compared with the actual repair used to repair the equipment. A mismatch is identified in response to any recommended repair not matching the actual repair. Reports are generated for displaying all of the identified mismatches. The reports are analyzed for determining repair codes having an increase in a number of anomalies. Service centers are alerted of a correct repair for the identified failure mode.
    • 检测设备维修数据中异常的方法。 故障模式 - 症状相关矩阵将故障模式与症状相关联。 收集诊断故障代码以进行设备的实际维修。 诊断故障代码被提供给用于识别故障模式的诊断推理器。 诊断评估由诊断推理器应用,用于确定对设备执行的建议修理以响应识别故障模式。 将每个推荐的维修与用于维修设备的实际维修进行比较。 鉴于任何推荐的维修与实际维修不匹配,确定不匹配。 生成报告以显示所有已识别的不匹配。 分析报告以确定具有异常数量增加的修复代码。 提醒服务中心对所识别的故障模式进行正确修复。