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公开(公告)号：US20110238258A1
公开(公告)日：2011-09-29
申请号：US12730883
申请日：2010-03-24
Applicant: Satnam Singh , Rahul Chougule , Pulak Bandyopadhyay
Inventor： Satnam Singh , Rahul Chougule , Pulak Bandyopadhyay
CPC classification number: G07C5/0808
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US08301333B2
公开(公告)日：2012-10-30
申请号：US12730883
申请日：2010-03-24
Applicant: Satnam Singh , Rahul Chougule , Pulak Bandyopadhyay
Inventor： Satnam Singh , Rahul Chougule , Pulak Bandyopadhyay
CPC classification number: G07C5/0808
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US20110137711A1
公开(公告)日：2011-06-09
申请号：US12630866
申请日：2009-12-04
Applicant: Satnam Singh , Pulak Bandyopadhyay , Calvin E. Wolf
Inventor： Satnam Singh , Pulak Bandyopadhyay , Calvin E. Wolf
CPC classification number: G06Q10/06 , G06F11/079 , G06Q10/0639 , G06Q10/20 , G07C5/0808
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US08996235B2
公开(公告)日：2015-03-31
申请号：US13295170
申请日：2011-11-14
Applicant: Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay , Pattada A. Kallappa , Robert J. Casper , Jeremy Krall , Joseph M. Martinez , Brian W. Quinn
Inventor： Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay , Pattada A. Kallappa , Robert J. Casper , Jeremy Krall , Joseph M. Martinez , Brian W. Quinn
CPC classification number: G05B23/0278
Abstract: A vehicle repair assist system for repairing a vehicle fault in a vehicle. A symptom input module is provided for entering vehicle symptom information relating to the fault. A diagnostic code module retrieves diagnostic trouble codes from the vehicle. The diagnostic trouble codes are generated in response to a fault occurrence. A knowledge-based fault module identifies potential causes of the vehicle fault based on at least one of the symptom information and diagnostic trouble codes. A repair assistant module identifies recommended repair parts and repair procedures for repairing the cause of the vehicle fault. The repair assistant module communicates with the knowledge-based fault module for obtaining a prioritized listing of the recommended repair parts and repair procedures for repairing the vehicle fault.
Abstract translation: 一种用于修理车辆中的车辆故障的车辆修理辅助系统。 提供症状输入模块，用于输入与故障相关的车辆症状信息。 诊断代码模块从车辆检索诊断故障代码。 诊断故障代码是根据故障发生而产生的。 基于知识的故障模块基于症状信息和诊断故障代码中的至少一个来识别车辆故障的潜在原因。 维修辅助模块识别修理车辆故障原因的推荐维修部件和修理程序。 维修辅助模块与基于知识的故障模块进行通信，以获得修复车辆故障的推荐维修部件和维修程序的优先列表。
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公开(公告)号：US20130124032A1
公开(公告)日：2013-05-16
申请号：US13295170
申请日：2011-11-14
Applicant: Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay , Pattada A. Kallappa , Robert J. Casper , Jeremy Krall , Joseph M. Martinez , Brian W. Quinn
Inventor： Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay , Pattada A. Kallappa , Robert J. Casper , Jeremy Krall , Joseph M. Martinez , Brian W. Quinn
IPC: G06F7/00
CPC classification number: G05B23/0278
Abstract: A vehicle repair assist system for repairing a vehicle fault in a vehicle. A symptom input module is provided for entering vehicle symptom information relating to the fault. A diagnostic code module retrieves diagnostic trouble codes from the vehicle. The diagnostic trouble codes are generated in response to a fault occurrence. A knowledge-based fault module identifies potential causes of the vehicle fault based on at least one of the symptom information and diagnostic trouble codes. A repair assistant module identifies recommended repair parts and repair procedures for repairing the cause of the vehicle fault. The repair assistant module communicates with the knowledge-based fault module for obtaining a prioritized listing of the recommended repair parts and repair procedures for repairing the vehicle fault.
Abstract translation: 一种用于修理车辆中的车辆故障的车辆修理辅助系统。 提供症状输入模块，用于输入与故障相关的车辆症状信息。 诊断代码模块从车辆检索诊断故障代码。 诊断故障代码是根据故障发生而产生的。 基于知识的故障模块基于症状信息和诊断故障代码中的至少一个来识别车辆故障的潜在原因。 维修辅助模块识别修理车辆故障原因的推荐维修部件和修理程序。 维修辅助模块与基于知识的故障模块进行通信，以获得修复车辆故障的推荐维修部件和维修程序的优先列表。
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Patent Agency Ranking
公开(公告)号：US20120151290A1
公开(公告)日：2012-06-14
申请号：US12964230
申请日：2010-12-09
Applicant: Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay
Inventor： Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay
CPC classification number: G05B23/0278
Abstract: A method and system for comparing and merging fault models which are derived from different data sources. Two or more fault models are first represented as bipartite weighted graphs, which define correlations between failure modes and symptoms. The nodes of the graphs are compared to find failure modes and symptoms which are the same even though the specific terminology may be different. A graph matching method is then used to compare the graphs and determine which failure mode and symptom correlations are common between them. Finally, smoothing techniques and domain expert knowledge are used to merge and update the fault models, producing an integrated fault model which can be used by onboard vehicle systems, service facilities, and others.
Abstract translation: 用于比较和合并来自不同数据源的故障模型的方法和系统。 首先将两个或更多个故障模型表示为二分加权图，其定义故障模式和症状之间的相关性。 将图的节点进行比较以找出相同的故障模式和症状，即使特定的术语可能不同。 然后使用图匹配方法来比较图，并确定它们之间的故障模式和症状相关性。 最后，使用平滑技术和领域专家知识来合并和更新故障模型，生成可由车载系统，服务设施等使用的集成故障模型。
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公开(公告)号：US09740993B2
公开(公告)日：2017-08-22
申请号：US12630866
申请日：2009-12-04
Applicant: Satnam Singh , Pulak Bandyopadhyay , Calvin E. Wolf
Inventor： Satnam Singh , Pulak Bandyopadhyay , Calvin E. Wolf
CPC classification number: G06Q10/06 , G06F11/079 , G06Q10/0639 , G06Q10/20 , G07C5/0808
Abstract: 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.
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公开(公告)号：US08645019B2
公开(公告)日：2014-02-04
申请号：US12964230
申请日：2010-12-09
Applicant: Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay
Inventor： Satnam Singh , Steven W. Holland , Pulak Bandyopadhyay
CPC classification number: G05B23/0278
Abstract: A method and system for comparing and merging fault models which are derived from different data sources. Two or more fault models are first represented as bipartite weighted graphs, which define correlations between failure modes and symptoms. The nodes of the graphs are compared to find failure modes and symptoms which are the same even though the specific terminology may be different. A graph matching method is then used to compare the graphs and determine which failure mode and symptom correlations are common between them. Finally, smoothing techniques and domain expert knowledge are used to merge and update the fault models, producing an integrated fault model which can be used by onboard vehicle systems, service facilities, and others.
Abstract translation: 用于比较和合并来自不同数据源的故障模型的方法和系统。 首先将两个或更多个故障模型表示为二分加权图，其定义故障模式和症状之间的相关性。 将图的节点进行比较以找出相同的故障模式和症状，即使特定的术语可能不同。 然后使用图匹配方法来比较图，并确定它们之间的故障模式和症状相关性。 最后，使用平滑技术和领域专家知识来合并和更新故障模型，生成可由车载系统，服务设施等使用的集成故障模型。
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9.发明授权公开(公告)号：US08498776B2
公开(公告)日：2013-07-30
申请号：US12620466
申请日：2009-11-17
Applicant: Satnam Singh , Vineet R. Khare , Rahul Chougule
Inventor： Satnam Singh , Vineet R. Khare , Rahul Chougule
IPC: G01M17/00
CPC classification number: G05B19/0428 , G05B2219/24076 , G05B2219/2637
Abstract: A system and method for fault diagnosis includes receiving information defining a relationship between failure modes and diagnostic trouble codes and extracting diagnostic trouble code data, including set times, frequency data and diagnostic trouble code sequence information for a plurality of diagnostic trouble codes relating to a plurality of failure modes. The system and method further include constructing a Markov chain using the diagnostic trouble code data for each of the plurality of failure modes, training the Markov chain to learn a set of state parameters using the diagnostic trouble code data, and computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained Markov chains.
Abstract translation: 用于故障诊断的系统和方法包括接收定义故障模式和诊断故障代码之间的关系的信息，并且提取诊断故障代码数据，包括用于多个诊断故障代码的多个诊断故障代码的设置时间，频率数据和诊断故障代码序列信息 的故障模式。 该系统和方法还包括使用针对多个故障模式中的每一个的诊断故障代码数据构建马尔可夫链，训练马尔可夫链以使用诊断故障代码数据学习一组状态参数，以及计算诊断的可能性 使用训练马尔科夫链的多个故障模式中的每一个的故障代码序列。
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10.发明申请公开(公告)号：US20110118932A1
公开(公告)日：2011-05-19
申请号：US12620466
申请日：2009-11-17
Applicant: Satnam Singh , Vineet R. Khare , Rahul Chougule
Inventor： Satnam Singh , Vineet R. Khare , Rahul Chougule
CPC classification number: G05B19/0428 , G05B2219/24076 , G05B2219/2637
Abstract: A system and method for fault diagnosis includes receiving information defining a relationship between failure modes and diagnostic trouble codes and extracting diagnostic trouble code data, including set times, frequency data and diagnostic trouble code sequence information for a plurality of diagnostic trouble codes relating to a plurality of failure modes. The system and method further include constructing a Markov chain using the diagnostic trouble code data for each of the plurality of failure modes, training the Markov chain to learn a set of state parameters using the diagnostic trouble code data, and computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained Markov chains.
Abstract translation: 用于故障诊断的系统和方法包括接收定义故障模式和诊断故障代码之间的关系的信息，并且提取诊断故障代码数据，包括用于多个诊断故障代码的多个诊断故障代码的设置时间，频率数据和诊断故障代码序列信息 的故障模式。 该系统和方法还包括使用针对多个故障模式中的每一个的诊断故障代码数据构建马尔可夫链，训练马尔可夫链以使用诊断故障代码数据学习一组状态参数，以及计算诊断的可能性 使用训练马尔科夫链的多个故障模式中的每一个的故障代码序列。
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