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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：US20120116630A1
公开(公告)日：2012-05-10
申请号：US12943261
申请日：2010-11-10
Applicant: Mark N. Howell , Mutasim A. Salman , Xidong Tang , Yilu Zhang , Soumen De , Sugato Chakrabarty , Rahul Chougule , Pulak Bandyopadhyay , Steven W. Holland
Inventor： Mark N. Howell , Mutasim A. Salman , Xidong Tang , Yilu Zhang , Soumen De , Sugato Chakrabarty , Rahul Chougule , Pulak Bandyopadhyay , Steven W. Holland
IPC: G06F19/00
CPC classification number: G06Q10/0639
Abstract: A method is provided for enhancing service diagnostics utilizing service repair data of previously serviced vehicles. Service repair data of previously serviced vehicles is obtained from a memory storage device. The service data is compiled into a service diagnostic code dataset and a service labor code dataset. The service diagnostic code dataset and service labor code dataset are categorized into an electronic data table. Respective combinations are formed in the electronic data table. An aggregate count is determined for each respective combination in the electronic data table. Either of a respective diagnostic code or a respective service labor code is identified having a correlation with more than one of either service diagnostic codes or service labor codes. At least one of a service repair procedure used to repair the vehicle or a respective service diagnostic code used to identify the fault is modified in response to analyzing the respective combinations.
Abstract translation: 提供了一种利用先前服务车辆的维修数据来增强服务诊断的方法。 从存储器存储装置获得先前服务车辆的维修数据。 服务数据被编译成服务诊断代码数据集和服务人工代码数据集。 服务诊断码数据集和服务人工代码数据集分为电子数据表。 在电子数据表中形成各种组合。 为电子数据表中的每个组合确定聚合计数。 识别与诊断代码或服务劳动代码中的多于一个的相关性的各个诊断代码或相应的服务劳工代码。 响应于分析各个组合，修改用于修理车辆的维修程序中的至少一个或用于识别故障的相应服务诊断代码。
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公开(公告)号：US08463485B2
公开(公告)日：2013-06-11
申请号：US12943261
申请日：2010-11-10
Applicant: Mark N. Howell , Mutasim A. Salman , Xidong Tang , Yilu Zhang , Soumen De , Sugato Chakrabarty , Rahul Chougule , Pulak Bandyopadhyay , Steven W. Holland
Inventor： Mark N. Howell , Mutasim A. Salman , Xidong Tang , Yilu Zhang , Soumen De , Sugato Chakrabarty , Rahul Chougule , Pulak Bandyopadhyay , Steven W. Holland
IPC: G01M17/00
CPC classification number: G06Q10/0639
Abstract: A method is provided for enhancing service diagnostics utilizing service repair data of previously serviced vehicles. Service repair data of previously serviced vehicles is obtained from a memory storage device. The service data is compiled into a service diagnostic code dataset and a service labor code dataset. The service diagnostic code dataset and service labor code dataset are categorized into an electronic data table. Respective combinations are formed in the electronic data table. An aggregate count is determined for each respective combination in the electronic data table. Either of a respective diagnostic code or a respective service labor code is identified having a correlation with more than one of either service diagnostic codes or service labor codes. At least one of a service repair procedure used to repair the vehicle or a respective service diagnostic code used to identify the fault is modified in response to analyzing the respective combinations.
Abstract translation: 提供了一种利用先前服务车辆的维修数据来增强服务诊断的方法。 从存储器存储装置获得先前服务车辆的维修数据。 服务数据被编译成服务诊断代码数据集和服务人工代码数据集。 服务诊断码数据集和服务人工代码数据集分为电子数据表。 在电子数据表中形成各种组合。 为电子数据表中的每个组合确定聚合计数。 识别与诊断代码或服务劳动代码中的多于一个的相关性的各个诊断代码或相应的服务劳工代码。 响应于分析各个组合，修改用于修理车辆的维修程序中的至少一个或用于识别故障的相应服务诊断代码。
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5.发明申请INTEGRATED DIAGNOSIS AND PROGNOSIS SYSTEM AS PART OF THE CORPORATE VALUE CHAIN 有权综合诊断和预防系统作为企业价值链的一部分公开(公告)号：US20100191414A1
公开(公告)日：2010-07-29
申请号：US12362364
申请日：2009-01-29
Applicant: Mutasim A. Salman , Pulak Bandyopadhyay , Sugato Chakrabarty , Rahul Chougule , Sabyasachi Bhattacharya , Soumen De , Debprakash Patnaik , Andrea M. Simon , Steven W. Holland , Yuen-Kwok Chin , Xiaodong Zhang , Yilu Zhang
Inventor： Mutasim A. Salman , Pulak Bandyopadhyay , Sugato Chakrabarty , Rahul Chougule , Sabyasachi Bhattacharya , Soumen De , Debprakash Patnaik , Andrea M. Simon , Steven W. Holland , Yuen-Kwok Chin , Xiaodong Zhang , Yilu Zhang
CPC classification number: G06Q10/06
Abstract: An integrated diagnosis and prognosis system that collects vehicle information over the life of a vehicle and its development. The system provides the collected vehicle information to supplier management, product development management, service/dealership management, customer relations departments and production facilities, which use the information to take certain action for existing vehicles, fleets of vehicles or future vehicles to improve vehicle reliability and quality.
Abstract translation: 一种综合诊断和预后系统，用于收集车辆生命周期中的车辆信息及其发展。 系统将收集的车辆信息提供给供应商管理，产品开发管理，服务/经销商管理，客户关系部门和生产设备，利用这些信息为现有车辆，车队或未来车辆采取某些行动，以提高车辆的可靠性， 质量。
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Patent Agency Ranking
6.发明授权Integrated diagnosis and prognosis system as part of the corporate value chain 有权综合诊断和预后系统作为企业价值链的一部分公开(公告)号：US08170743B2
公开(公告)日：2012-05-01
申请号：US12362364
申请日：2009-01-29
Applicant: Mutasim A. Salman , Pulak Bandyopadhyay , Sugato Chakrabarty , Rahul Chougule , Sabyasachi Bhattacharya , Soumen De , Debprakash Patnaik , Andrea M. Simon , Steven W. Holland , Yuen-Kwok Chin , Xiaodong Zhang , Yilu Zhang
Inventor： Mutasim A. Salman , Pulak Bandyopadhyay , Sugato Chakrabarty , Rahul Chougule , Sabyasachi Bhattacharya , Soumen De , Debprakash Patnaik , Andrea M. Simon , Steven W. Holland , Yuen-Kwok Chin , Xiaodong Zhang , Yilu Zhang
CPC classification number: G06Q10/06
Abstract: An integrated diagnosis and prognosis system that collects vehicle information over the life of a vehicle and its development. The system provides the collected vehicle information to supplier management, product development management, service/dealership management, customer relations departments and production facilities, which use the information to take certain action for existing vehicles, fleets of vehicles or future vehicles to improve vehicle reliability and quality.
Abstract translation: 一种综合诊断和预后系统，用于收集车辆生命周期中的车辆信息及其发展。 系统将收集的车辆信息提供给供应商管理，产品开发管理，服务/经销商管理，客户关系部门和生产设备，利用这些信息为现有车辆，车队或未来车辆采取某些行动，以提高车辆的可靠性， 质量。
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7.发明申请METHOD AND PROCESS FOR AUTOMATIC GENERATION OF SYMPTOM CODES FROM TEXTUAL PROBLEM DESCRIPTIONS TO ENABLE PROBLEM CLASSIFICATION, EARLY WARNING TREND PREDICTION, AND FAST RECALL OF PROGNOSTIC/DIAGNOSTIC SOLUTIONS 审中-公开自动生成症状代码的方法和过程，从文本问题描述到启用问题分类，早期警告趋势预测和快速恢复预防/诊断解决方案公开(公告)号：US20090265364A1
公开(公告)日：2009-10-22
申请号：US12104010
申请日：2008-04-16
Applicant: Alexander P. Morgan , Steven W. Holland , Andrea M. Simon , Pulak Bandyopadhyay , Sugato Chakrabarty , Rahul Chougule
Inventor： Alexander P. Morgan , Steven W. Holland , Andrea M. Simon , Pulak Bandyopadhyay , Sugato Chakrabarty , Rahul Chougule
IPC: G06F17/30
CPC classification number: G06Q10/10
Abstract: A system and method for converting text related to vehicle service to symptom codes. The method includes typing into work orders and service reports statements that describe the various symptoms and problems of a vehicle that is being serviced. The work orders and service reports are then transmitted to a database facility where they are analyzed. Prior to the reports being analyzed, the text in the work order and service reports is read by a machine reader that converts the text to symptom codes that describe particular vehicle conditions and symptoms. A processor analyzes the codes for patterns and other relationships, and can provide a display of such patterns. Further, the codes and reports are stored in a memory.
Abstract translation: 将与车辆服务相关的文本转换为症状代码的系统和方法。 该方法包括键入描述正在服务的车辆的各种症状和问题的工作单和服务报告语句。 然后将工单和服务报告传送到数据库设施进行分析。 在分析报告之前，工单和服务报告中的文本由机器阅读器读取，将文本转换为描述特定车辆状况和症状的症状代码。 处理器分析模式和其他关系的代码，并且可以提供这样的模式的显示。 此外，代码和报告存储在存储器中。
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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：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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