EP1307816A1 - System for determining error causes - Google PatentsSystem for determining error causes
- Publication number
- EP1307816A1 EP1307816A1 EP20000960421 EP00960421A EP1307816A1 EP 1307816 A1 EP1307816 A1 EP 1307816A1 EP 20000960421 EP20000960421 EP 20000960421 EP 00960421 A EP00960421 A EP 00960421A EP 1307816 A1 EP1307816 A1 EP 1307816A1
- European Patent Office
- Prior art keywords
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- 238000000034 methods Methods 0 description 12
- 238000005365 production Methods 0 description 1
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
- G06F11/2257—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing using expert systems
System for determining causes of errors
The invention relates to a system for determining the cause of failures, including a computer-aided generation of hypotheses and to perform its verification as part of a fault cause analysis. The system is designed to support the cause of the error search in the case of incoming error events in industrial plants.
For the fault cause analysis of different methods and techniques can be used. These include the Fault Tree Analysis (Fault Tree Analysis) or the error cause analysis (root cause analysis) in various versions, such as described in [Reliability Center, Inc .: Root Cause Failure Analysis Methods, Hopewell, United States, 1997] or [ASB Group Inc. , Risk and Reliability Division: root Cause Analysis Handbook: A Guide to Effective Incident Investigation, Knox ville, USA, 1999].
In such a fault cause analysis, the following steps are typically carried out:
1. Carry out an FMEA (Failure Mode And Effects Analysis) to determine the really important mistakes.
2. carry out the actual cause of the error analysis for each of the significant errors. The following sub-steps are carried out: a) ensuring all necessary information which allow conclusions about the course and causes of the error, for example, damaged components, capable of the same, interviews with operators, process data, etc. b) Organization of failure analysis. Determining the necessary resources and implementation plan. c) actual analysis, for example, based on fault tree and determining the error causes. Here, fault trees are set up with which the error causes can be determined based on the error event over several levels cause.
3. implementing the improvements. For this, the identified fault causes and recommendations for corrective actions to the appropriate decision makers to communicate and it is established an action plan under which the recommendations are carried out. Could this action plan will be successfully completed, the analysis is finished.
Hypotheses for causes of errors are needed in step 2c if repeated starting from the fault event the question is asked, could lead to the error event. Hypotheses are therefore speculate on the cause of the error based on empirical knowledge. hypotheses to be verified by the previously seized information will be used on the error and thus the hypothesis can be confirmed or refuted. If a hypothesis is confirmed in this way, the situation described by the hypothesis to a fact or a fact. For this fact, in turn, the question may be asked, could lead to this fact, that it be placed more hypotheses.
A disadvantage of the known method is that the creation of hypotheses and their verification depends on the experience of the error or the analysts. Even if appropriate fault trees, generated from fault models, for example, are present, they are not directly usable for the present failure analysis. Rather have such errors trees and also the hypotheses contained therein to the particular, be adapted to be examined error event. For this purpose, an experienced analyst error is necessary.
The invention has for its object to provide a system for determining the causes of errors that enables automated determination of hypotheses and their verification as part of a requisite fault cause analysis. This object is achieved by a system for determining the causes of errors that includes an automated determination of hypotheses and their verification, and having the features indicated in claim. 1 Advantageous embodiments are indicated in further claims.
The system supports a user in the analysis of selected fault events by making proposals for the execution of a general fault model. These proposals relate to an appropriate entry point in the model and in particular suitable hypothesis and verification options that enable efficient implementation of the fault cause analysis. To use the system experiences gained in already carried out error analyzes and are available in a library experience.
A further description of the invention and the advantages thereof will be given below with reference to embodiments which are illustrated in the drawing figures.
Fig. 1 is a block diagram of a system according to the invention for automatic generation of hypotheses and their verification for determining the causes of errors, Fig. 2 is an illustration of the operation of the system,
Fig. 3 is a description of the current error event,
Fig. 4 shows a schematic representation of the general fault model,
Fig. 5 is a diagram for categorization of the error models,
Fig. 6 essential contents of a fault hypothesis,
Fig. 7 successive referencing error models, and
Fig. 8 shows the integration of a hypothesis information in the execution of the overall error model.
The proposed system can be implemented locally in a computer and used. Preferably, however, an implementation of the Internet, because then easily worldwide use for service purposes is possible. Fig. 1 shows the diagram of a system for automatic generation of hypotheses and their verification for determining the cause of failures. The system is divided into means for data input and visualization 10, a data processing device 20 and a data memory 30. As a means for file input and visualization is a common Web browser 11 is used. The data processing device 20 includes a processing unit for fault cause analysis 21, which coordinates the execution of the fault cause analysis. For processing additional components are available that support the detailed steps, namely a first comparator 22, a second comparator 23 and a hypothesis-selection 24. The processing unit for fault cause analysis 21 uses data from the data memory 30 in which an access to a fault events list 32, a general fault model 33 and an experience database takes place 34th Intermediate and final results of failure analysis are stored in a log current fault cause analysis 31st In the log 31 are added: the respectively selected current error event description, a suitable entry point in the overall error model, selected experience failure analysis, a list of selected information fault hypotheses and the result of the fault cause analysis, that is, the error causes determined. User input is requested by the processing unit for fault cause analysis 21 via the Web browser. 11 Results are presented to the user via the web browser. 11
Fig. 2 shows a flow diagram illustrating the operation of the system.
In step 100, the user through the web browser interface 11 selects one to be examined failure event from the list of error events 32 and stores it in the component log current fault cause analysis 31 from. The description of this current error event is discussed in more detail in Fig. 3.
In step 200, the system compares by means of the first comparator 22, the description of the current error event (see Fig. 3) with the contents of the general fault model 33 and proposes entry points to the error analysis before. The system uses this common attributes describing the current error event and the general fault model, such as error title, error text effects Auswir-, error code and error location, and checked the values of equality and similarity. A suitable method that performs this check is, for example, the nearest neighbor method [I. Watson: Applying Case-Based Reasoning: Techniques for Enterprise Systems, Morgan Kaufmann Publishers, Inc., San Francisco, 1997, pages 23 to 33]. The structure of the general fault model, Figs. 4, Fig. 6 and Fig. 7. If the system suggests more suitable fault trees as the entry point for the failure analysis, the user selects the most appropriate. The system saves a description of the entry point in the component log current fault cause analysis 31st
In step 300, the system compares by means of the second comparator 23, the description of the current error event with the experience failure analysis of the empirical database 34 and proposes suitable experience failure analysis. Similar to step 200, the system compares here the common attributes of the current error event, and the experience failure analysis. The nearest neighbor method is suitable also for the similarity test. An experience error analysis shows how and with what result, a failure event was analyzed. Predominately summarized the key during the analysis hypotheses and their verification in a list. The user selects the most appropriate experience failure analysis. The system stores this experience failure analysis in the component log current fault cause analysis 31 from.
In step 400, the system conceived by the component hypotheses selection 24 lists all the selected experience hypotheses together and suggests the user to candidates for a current error analysis before. The hypotheses are having particular regard to the
occurred in many fault analysis, had a key role in the cause identification, for example because the relevant verifications had a special significance, were considered especially important by the practitioner, for example, because an investigation into an entire complex of causes could be excluded, or, with very little effort had to be verified. The user selects those hypotheses, which he wants to use as part of the current error analysis. The system stores this experience hypotheses in the component log current fault cause analysis 31 from.
In step 500, the selected in step 400 hypotheses in the processing of the general fault model are integrated. To perform the error analysis, the system follows the general fault model and processes the hypotheses contained therein. For each hypothesis, the system checks by comparison of the attributes if appropriate experience hypotheses exist. In this case, the information including hypothesis verification is used, otherwise the hypothesis is maintained from the general model. The system stores the causes of errors detected as the result of the fault cause analysis in the component current protocol fault cause analysis 31..
Fig. 3 shows the view of a current error event. For this purpose, the error event in the attributes error text effects of the error, time at which the error occurred, the error code used internally in the error code list and the fault will be described. In addition, reference is made to other system records that reflect the process situation at the time of the error. This includes the process data, as recorded by the control system, the operator log and the work reports from the Maintenance Management System.
Fig. 4 shows the schematic view of a fault model. The uppermost level includes a process model having a plurality of process steps. Each process step can be further divided into process steps. there are error events and critical process components to each process step.
The next lower level of the model includes fault trees. Fault trees are shown in FIG. 5 can be seen, categorized. A fault tree can be composed of several sub-trees. This is indicated in Fig. 5 by the arrows and is explained in more detail in Fig. 7. The nodes of a fault tree represent fault hypotheses. Essential content component of a fault hypothesis is a checklist for verification. The contents of a hypothesis is discussed in more detail in Fig. 6.
Fig. 5 shows the classification of fault models. The prepared thereon industry-specific fault models are features of a particular industry, such as fault models for the cement industry or error models for the steel industry. This category of fault models has as the top node the error event. An error event is an undesirable condition that affects the production.
A second category of FIG. 5 describes component faults. These models are universal, so are applicable in different industries. Typically refer hypotheses of industry-specific fault models for component failures models.
The third category of fault models describes very generally applicable fault relationships. Such errors, for example, have their roots in inadequate training of staff, organizational grievances or problems in maintenance. These models are underlain the industry-specific fault models and component fault models.
Fig. 6 shows the essential contents of a fault hypothesis in the example of a cleaning system of steel plates. The hypothesis includes a description of fault relationships. In addition, the affected component or the affected part-system of the hypothesis is associated. A checklist describes criteria such as the hypothesis can be verified. For each criterion, the cost of diagnosis is specified. The hypothesis may be underlain complex independent fault trees. This reference is in the fault tree reference.
Fig. 7 shows an example of the error event 'Unzureichenende product quality' as fault trees refer to each other and complex and extensive fault relationships can be modeliiert as in this manner. Fig. 8 illustrates the example of FIG. 7 engages and shows how the fault hypothesis 'error in the cleaning system' when processing the general fault model is replaced by a fitting experience hypothesis. The entire fault tree (as shown left), by the far smaller fault tree the experience hypothesis be replaced, which can significantly reduce the overhead of processing (right in the illustration).
Priority Applications (2)
|Application Number||Priority Date||Filing Date||Title|
|PCT/EP2000/007730 WO2002013015A1 (en)||2000-08-09||2000-08-09||System for determining error causes|
|US10/364,004 US6952658B2 (en)||2000-08-09||2003-02-10||System for determining fault causes|
|Publication Number||Publication Date|
|EP1307816A1 true EP1307816A1 (en)||2003-05-07|
Family Applications (1)
|Application Number||Title||Priority Date||Filing Date|
|EP20000960421 Withdrawn EP1307816A1 (en)||2000-08-09||2000-08-09||System for determining error causes|
Country Status (4)
|US (1)||US6952658B2 (en)|
|EP (1)||EP1307816A1 (en)|
|AU (1)||AU7273900A (en)|
|WO (1)||WO2002013015A1 (en)|
Families Citing this family (34)
|Publication number||Priority date||Publication date||Assignee||Title|
|DE10146901A1 (en) *||2001-09-24||2003-05-15||Abb Research Ltd||Method and system for processing error hypotheses|
|US7230812B2 (en) *||2003-11-21||2007-06-12||Agere Systems Inc||Predictive applications for devices with thin dielectric regions|
|DE102004007053A1 (en) *||2004-02-13||2005-09-15||Daimlerchrysler Ag||Automatic test case generation method for testing a technical system in which a fault tree and an unwanted result are provided and from this combinations of causes that could cause the result are systematically determined|
|US7412842B2 (en)||2004-04-27||2008-08-19||Emerson Climate Technologies, Inc.||Compressor diagnostic and protection system|
|US7275377B2 (en)||2004-08-11||2007-10-02||Lawrence Kates||Method and apparatus for monitoring refrigerant-cycle systems|
|DE102004041898A1 (en) *||2004-08-30||2006-03-09||Siemens Ag||Method and device for diagnosis in service systems for technical installations|
|US20060095230A1 (en) *||2004-11-02||2006-05-04||Jeff Grier||Method and system for enhancing machine diagnostics aids using statistical feedback|
|US20070061110A1 (en) *||2005-09-09||2007-03-15||Canadian Space Agency||System and method for diagnosis based on set operations|
|US8590325B2 (en)||2006-07-19||2013-11-26||Emerson Climate Technologies, Inc.||Protection and diagnostic module for a refrigeration system|
|US20080216494A1 (en)||2006-09-07||2008-09-11||Pham Hung M||Compressor data module|
|DE102006056879A1 (en)||2006-12-01||2008-06-05||Dürr Systems GmbH||Error logging procedure for a coating plant|
|JP5075465B2 (en) *||2007-04-20||2012-11-21||株式会社東芝||Incident / accident report analysis apparatus, method, and program|
|US20090037142A1 (en)||2007-07-30||2009-02-05||Lawrence Kates||Portable method and apparatus for monitoring refrigerant-cycle systems|
|US9140728B2 (en)||2007-11-02||2015-09-22||Emerson Climate Technologies, Inc.||Compressor sensor module|
|US20100131334A1 (en) *||2008-11-21||2010-05-27||Searete Llc, A Limited Liability Corporation Of The State Of Delaware||Hypothesis development based on selective reported events|
|US8224842B2 (en) *||2008-11-21||2012-07-17||The Invention Science Fund I, Llc||Hypothesis selection and presentation of one or more advisories|
|US20100131607A1 (en) *||2008-11-21||2010-05-27||Searete Llc, A Limited Liability Corporation Of The State Of Delaware||Correlating data indicating subjective user states associated with multiple users with data indicating objective occurrences|
|US8260912B2 (en) *||2008-11-21||2012-09-04||The Invention Science Fund I, Llc||Hypothesis based solicitation of data indicating at least one subjective user state|
|US8244858B2 (en) *||2008-11-21||2012-08-14||The Invention Science Fund I, Llc||Action execution based on user modified hypothesis|
|US8260729B2 (en) *||2008-11-21||2012-09-04||The Invention Science Fund I, Llc||Soliciting data indicating at least one subjective user state in response to acquisition of data indicating at least one objective occurrence|
|US8239488B2 (en) *||2008-11-21||2012-08-07||The Invention Science Fund I, Llc||Hypothesis development based on user and sensing device data|
|US8224956B2 (en) *||2008-11-21||2012-07-17||The Invention Science Fund I, Llc||Hypothesis selection and presentation of one or more advisories|
|US8813025B1 (en) *||2009-01-12||2014-08-19||Bank Of America Corporation||Customer impact predictive model and combinatorial analysis|
|US8832657B1 (en) *||2009-01-12||2014-09-09||Bank Of America Corporation||Customer impact predictive model and combinatorial analysis|
|CN105910247B (en)||2011-02-28||2018-12-14||艾默生电气公司||The monitoring and diagnosis of the HVAC of house solution|
|US8964338B2 (en)||2012-01-11||2015-02-24||Emerson Climate Technologies, Inc.||System and method for compressor motor protection|
|US9310439B2 (en)||2012-09-25||2016-04-12||Emerson Climate Technologies, Inc.||Compressor having a control and diagnostic module|
|CN103049346B (en) *||2012-12-11||2015-03-18||工业和信息化部电子第五研究所||Failure physics based component fault tree construction method and system|
|US9803902B2 (en)||2013-03-15||2017-10-31||Emerson Climate Technologies, Inc.||System for refrigerant charge verification using two condenser coil temperatures|
|US9551504B2 (en)||2013-03-15||2017-01-24||Emerson Electric Co.||HVAC system remote monitoring and diagnosis|
|WO2014144446A1 (en)||2013-03-15||2014-09-18||Emerson Electric Co.||Hvac system remote monitoring and diagnosis|
|EP2981772A4 (en)||2013-04-05||2016-11-30||Emerson Climate Technologies||Heat-pump system with refrigerant charge diagnostics|
|US20150025866A1 (en) *||2013-07-22||2015-01-22||Honeywell International Inc.||Methods and apparatus for the creation and use of reusable fault model components|
|US9959158B2 (en)||2015-10-13||2018-05-01||Honeywell International Inc.||Methods and apparatus for the creation and use of reusable fault model components in fault modeling and complex system prognostics|
Family Cites Families (10)
|Publication number||Priority date||Publication date||Assignee||Title|
|US4866635A (en) *||1987-10-19||1989-09-12||Carnegie Group Inc.||Domain independent shell for building a diagnostic expert system|
|JP2547069B2 (en) *||1988-04-20||1996-10-23||富士通株式会社||Fault diagnosis system|
|US5067099A (en) *||1988-11-03||1991-11-19||Allied-Signal Inc.||Methods and apparatus for monitoring system performance|
|US4954964A (en) *||1988-11-09||1990-09-04||Singh Gurvinder P||Apparatus and method for expert analysis of metal failure with automated visual aide|
|EP0508571A3 (en) *||1991-03-12||1993-12-15||Hewlett Packard Co||Expert system to diagnose data communication networks|
|US6049792A (en) *||1993-03-19||2000-04-11||Ricoh Company Limited||Automatic invocation of computational resources without user intervention across a network|
|US5793933A (en) *||1993-09-13||1998-08-11||Kabushiki Kaisha Toshiba||Computer-implemented system and method for constructing a system|
|US5566092A (en) *||1993-12-30||1996-10-15||Caterpillar Inc.||Machine fault diagnostics system and method|
|WO2001055805A1 (en) *||2000-01-29||2001-08-02||Abb Research Ltd.||System and method for determining the overall equipment effectiveness of production plants, failure events and failure causes|
|AT363091T (en) *||2000-07-22||2007-06-15||Abb Research Ltd||System for supporting an error story analysis|
- 2003-02-10 US US10/364,004 patent/US6952658B2/en not_active Expired - Fee Related
Non-Patent Citations (1)
|See references of WO0213015A1 *|
Also Published As
|Publication number||Publication date|
|Colin et al.||Interpretive structural modeling of supply chain risks|
|Sharma et al.||A literature review and future perspectives on maintenance optimization|
|US9389988B1 (en)||Method and system for authorization based routing of failed test scenarios|
|US8407081B1 (en)||Method and system for improving effciency in an organization using process mining|
|Kemerer et al.||An empirical approach to studying software evolution|
|Smyth et al.||Adaptation-guided retrieval: questioning the similarity assumption in reasoning|
|US7103610B2 (en)||Method, system and computer product for integrating case based reasoning data and failure modes, effects and corrective action data|
|US5463768A (en)||Method and system for analyzing error logs for diagnostics|
|US6587833B1 (en)||Computational workload-based hardware sizer method, system and program product|
|US6343236B1 (en)||Method and system for analyzing fault log data for diagnostics|
|CA2387929C (en)||Method and apparatus for diagnosing difficult to diagnose faults in a complex system|
|Ahituv et al.||A system development methodology for ERP systems|
|US6795935B1 (en)||Diagnosis of faults in a complex system|
|FI125091B (en)||A system that allows the receipt inspection report|
|US7707058B2 (en)||Predicting parts needed for an onsite repair using expected waste derived from repair history|
|US7197427B2 (en)||Method for risk based testing|
|DE60005861T2 (en)||Method and system for analyzing continuous parameter data for diagnostics and repairs|
|Labib||World‐class maintenance using a computerised maintenance management system|
|US7730020B2 (en)||Diagnosis of equipment failures using an integrated approach of case based reasoning and reliability analysis|
|US6748304B2 (en)||Method and apparatus for improving fault isolation|
|Khanlari et al.||Prioritizing equipments for preventive maintenance (PM) activities using fuzzy rules|
|EP0413485B1 (en)||Performance improvement tool for rule based expert systems|
|US6532426B1 (en)||System and method for analyzing different scenarios for operating and designing equipment|
|US6988011B2 (en)||Method and system for analyzing operational parameter data for diagnostics and repairs|
|Garg et al.||Maintenance management: literature review and directions|
|AK||Designated contracting states:||
Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE
|17P||Request for examination filed||
Effective date: 20030109
|AX||Extension or validation of the european patent to||
Countries concerned: ALLTLVMKROSI
|17Q||First examination report||
Effective date: 20040705
|18D||Deemed to be withdrawn||
Effective date: 20050118