公开(公告)号：US20130091139A1

公开(公告)日：2013-04-11

申请号：US13267173

申请日：2011-10-06

Applicant: Dnyanesh RAJPATHAK ,  Vineet R. KKARE ,  Rahul CHOUGULE

Inventor： Dnyanesh RAJPATHAK ,  Vineet R. KKARE ,  Rahul CHOUGULE

IPC: G06F17/30

CPC classification number: G06F17/2785 ,  G10L15/1822

Abstract: A document may be received at a processing module. One or more tags may be applied to the document, each tag applied to a term, each tag representing a part of speech. One or more terms may be extracted from the document based on the tag. A weighting assignment parameter may be determined for each of the one or more extracted terms. Based on the weighting assignment parameter associated with each of the extracted terms, it may be determined whether the domain ontology includes the one or more extracted terms. If the domain ontology does not include the one or more extracted terms, the domain ontology may be augmented such that the domain ontology comprises the one or more extracted terms.

Abstract translation: 可以在处理模块处接收文档。 可以将一个或多个标签应用于文档，每个标签应用于术语，每个标签表示一部分语音。 可以基于标签从文档中提取一个或多个术语。 可以针对所提取的一个或多个术语中的每一个确定加权分配参数。 基于与所提取的每个项相关联的加权分配参数，可以确定域本体是否包括一个或多个提取的项。 如果域本体不包括一个或多个提取的术语，则域本体可以被扩充，使得域本体包括一个或多个提取的术语。

公开(公告)号：US08666982B2

公开(公告)日：2014-03-04

申请号：US13267173

申请日：2011-10-06

Applicant: Dnyanesh Rajpathak ,  Vineet R Khare ,  Rahul Chougule

Inventor： Dnyanesh Rajpathak ,  Vineet R Khare ,  Rahul Chougule

IPC: G06F17/30

CPC classification number: G06F17/2785 ,  G10L15/1822

Abstract: A document may be received at a processing module. One or more tags may be applied to the document, each tag applied to a term, each tag representing a part of speech. One or more terms may be extracted from the document based on the tag. A weighting assignment parameter may be determined for each of the one or more extracted terms. Based on the weighting assignment parameter associated with each of the extracted terms, it may be determined whether the domain ontology includes the one or more extracted terms. If the domain ontology does not include the one or more extracted terms, the domain ontology may be augmented such that the domain ontology comprises the one or more extracted terms.

Abstract translation: 可以在处理模块处接收文档。 可以将一个或多个标签应用于文档，每个标签应用于术语，每个标签表示一部分语音。 可以基于标签从文档中提取一个或多个术语。 可以针对所提取的一个或多个术语中的每一个确定加权分配参数。 基于与所提取的每个项相关联的加权分配参数，可以确定域本体是否包括一个或多个提取的项。 如果域本体不包括一个或多个提取的术语，则域本体可以被扩充，使得域本体包括一个或多个提取的术语。

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。

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

IPC: G06F7/00 ,  G01M17/00 ,  G06F17/10 ,  G06N3/02

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。

公开(公告)号：US20090295559A1

公开(公告)日：2009-12-03

申请号：US12131347

申请日：2008-06-02

Applicant: Mark N. Howell ,  Mutasim A. Salman ,  Xidong Tang ,  Xiaodong Zhang ,  Yilu Zhang ,  Yuen-Kwok Chin ,  William C. Lin ,  Rami I. Debouk ,  Steven W. Holland ,  Sugato Chakrabarty ,  Rahul Chougule

Inventor： Mark N. Howell ,  Mutasim A. Salman ,  Xidong Tang ,  Xiaodong Zhang ,  Yilu Zhang ,  Yuen-Kwok Chin ,  William C. Lin ,  Rami I. Debouk ,  Steven W. Holland ,  Sugato Chakrabarty ,  Rahul Chougule

IPC: B60Q1/00

CPC classification number: B60Q11/00 ,  B60R2021/01184 ,  B60W30/02 ,  B60W50/0205

Abstract: A system and method for determining the root cause of a fault in a vehicle system, sub-system or component using models and observations. In one embodiment, a hierarchical tree is employed to combine trouble or diagnostic codes from multiple sub-systems and components to get a confidence estimate of whether a certain diagnostic code is accurately giving an indication of problem with a particular sub-system or component. In another embodiment, a hierarchical diagnosis network is employed that relies on the theory of hierarchical information whereby at any level of the network only the required abstracted information is being used for decision making. In another embodiment, a graph-based diagnosis and prognosis system is employed that includes a plurality of nodes interconnected by information pathways. The nodes are fault diagnosis and fault prognosis nodes for components or sub-systems, and contain fault and state-of-health diagnosis and reasoning modules.

Abstract translation: 一种用于确定车辆系统，子系统或部件中使用模型和观测值的故障的根本原因的系统和方法。 在一个实施例中，使用分层树来组合来自多个子系统和组件的故障或诊断代码，以得到某一诊断代码是否准确地给出特定子系统或组件的问题的指示的置信度估计。 在另一个实施例中，采用依赖于层次信息理论的分级诊断网络，其中在网络的任何级别仅仅将所需的抽象信息用于决策。 在另一个实施例中，采用基于图的诊断和预后系统，其包括通过信息路径互连的多个节点。 节点是组件或子系统的故障诊断和故障预测节点，并包含故障和状态健康诊断和推理模块。

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。

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

IPC: G06F7/00 ,  G06F15/18 ,  G06N5/02 ,  G06F11/07

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。

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

IPC: G06F19/00 ,  G06F17/30

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。

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。

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

IPC: G01M17/00 ,  G06F7/00 ,  G06F19/00

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。