USRE45815E1 - Method for simplified real-time diagnoses using adaptive modeling - Google Patents

Method for simplified real-time diagnoses using adaptive modeling Download PDF

Info

Publication number
USRE45815E1
USRE45815E1 US12291990 US29199008A USRE45815E US RE45815 E1 USRE45815 E1 US RE45815E1 US 12291990 US12291990 US 12291990 US 29199008 A US29199008 A US 29199008A US RE45815 E USRE45815 E US RE45815E
Authority
US
Grant status
Grant
Patent type
Prior art keywords
output
phase
system
adaptive model
model
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.)
Active
Application number
US12291990
Inventor
Christof Nitsche
Stefan Schroedl
Wolfgang Weiss
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
OL Security LLC
Original Assignee
Ramsle Tech Group GmbH LLC
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Grant date

Links

Images

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0229Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL
    • B60L11/00Electric propulsion with power supplied within the vehicle
    • B60L11/18Electric propulsion with power supplied within the vehicle using power supply from primary cells, secondary cells, or fuel cells
    • B60L11/1881Fuel cells monitoring or controlling; Arrangements of fuel cells, structures or switching circuits therefore
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL
    • B60L3/00Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration, power consumption
    • B60L3/0023Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
    • B60L3/0046Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60LELECTRIC EQUIPMENT OR PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES, IN GENERAL
    • B60L3/00Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration, power consumption
    • B60L3/0023Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
    • B60L3/0053Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to fuel cells
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B60VEHICLES IN GENERAL
    • B60WCONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205Diagnosing or detecting failures; Failure detection models
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/0227Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
    • G05B23/0235Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/30Application of fuel cell technology to transportation
    • Y02T90/34Fuel cell powered electric vehicles [FCEV]

Abstract

A method for on-board real-time diagnostics of a mobile technical system using an adaptive technique to approximate stationary characteristic curves resulting from a workshop test. This adaptive technique uses observed non-stationary normal driving data to eliminate confounding variables.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a reissue of U.S. patent application Ser. No. 10/855,315 (now U.S. Pat. No. 7,136,779), filed May 28, 2004, which is hereby incorporated by reference.

BACKGROUND AND SUMMARY OF THE INVENTION

The present invention relates to on-board real-time diagnostics of mobile technical systems.

In order to detect faults or monitor ageing processes in vehicle systems, the normal procedure involves bringing the system into a mechanical workshop where the behavior can be tested using predefined and controlled conditions. Design tolerances and references can then be compared with measured variables to provide an accurate estimate concerning not only individual items but also the overall functioning and degradation of the system.

An internal combustion engine can be characterized by an engine speed/torque curve. A corresponding analysis tool for a fuel cell powertrain is a polarization curve as shown in FIG. 2. This polarization curve shows the effect of discharging current from a fuel cell system on the cell voltage and power. The curve is usually derived from a specifically designed dynamometer test cycle where the current and voltage are recorded at predefined static load points. The polarization curve, such as shown FIG. 2, results from an interpolation of those static load points.

The present invention results from a recognition that accomplishing of this diagnostics on an on-board component in real-time during normal driving would be a valuable tool not only for customers and field technicians, but also for development engineers. The ability to have a real-time diagnostics would lead to lower maintenance cost, faster problem resolution and shorter design cycles. It has also been recognized that the task of such on-line diagnostics is very complex with a principle obstacle being the range of varying dynamic influences. For example, with fuel cell stacks, the operational temperature, air/hydrogen gas temperatures and pressures inside the stack and the recordings of the fuel cell voltage and current lead to a range of uncertainty of the measurement points instead of more defined points recorded at predefined static loads. This comparison can be seen in FIG. 3 which compares work bench test data with data during normal driving.

This range of uncertainty in the factors can be attributed to both the external environment as well as control strategies of different system components. The system is rarely in equilibrium. As an example, the polarization of a fuel cell depends not only on the current load request, but also on the pressure on the air and hydrogen side. Furthermore the system behaves quite differently at the same point in the load diagram during positive and negative load changes.

As a result, the task of on-board diagnostics is significantly more complex than the stationary diagnostics because of a series of confounding variables.

It is an object of the present invention to provide on-board diagnostics of such a system in real-time during normal driving which leads to lower maintenance costs, quicker response time for problem resolution and shorter design cycles.

According to the present invention, known adaptive techniques are applied to estimate static characteristic curves such as those observed in a workshop test facility based on observed, non-stationary everyday driving data. As a result, the aforementioned confounding variables are eliminated with a resulting estimated characteristic curve which can be compared to a reference curve.

Other objects, advantages and novel features of the present invention will become apparent from the following detailed description of the invention when considered in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system architecture for providing real-time diagnostics according to the present invention;

FIG. 2 is a polarization curve illustrating the effect of discharging current from a fuel cell system on the cell voltage and power;

FIG. 3 illustrates a comparison of fuel cell voltage and current between a real-world driving cycle measurement and a stationary test measurement; and

FIG. 4 illustrates a comparison of data from a stationary test and from neural network prediction during real-world operation, according to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The reference model 11 of FIG. 1 contains a design specification for reference behavior of the vehicle component 7 in terms of prescribed output variables 6 which can include, for example, the fuel cell output, as a function of a number of independent and/or input variables 1. Examples of these input independent variables are gas pressures and gas flows. During normal driving operations, a number of additional confounding variables 2, such as the outside temperature, blur the clear functional relationship which would exist if the device were bench tested in a workshop.

The present invention has a goal of estimating the input-output behavior of the vehicle component operating under the reference input conditions, based on its currently observed behavior with varying environmental conditions. With such a predictive curve, the diagnostic module 12 functions to reduce the detected deviations from a stored “ideal” curve. The detection of these deviations is accomplished by the adaptive module component 8 which is implemented using any one of a series of machine learning techniques known in the art such as described in Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hart et al and Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation by Ian H. Witten and Eibe Franks. Generally speaking the learning component can be model-based, black-box, or a hybrid between these two extremes. Model based diagnosis has difficulty with complex technical systems because, even with a complete specification, it is difficult to tune the large number of parameters in order to realistically capture observed dependencies. The present invention uses an approach which employs general-purpose function modeling with an informed choice of the relevant input and output attributes. Therefore, by using adaptive curve fitting techniques in this manner it is possible to capture the “characteristic curves” of a system while also having the added benefit of being able to be used in multidimensional spaces as well as for continuous ranges of all input variables. In a particular embodiment for fuel cell application, the present invention uses the class of three-layer feed-forward neural networks.

The learning component is fed not only with the characteristic independent variables 1, but also with the confounding variables 2 (such as outside temperature). The system is able to assume an online learning scenario where training and diagnostics phases are interleaved using switch 5. The adaptive model 8 constantly tracks the current input-output behavior with the difference comparator 14, providing the difference between the predicted output and the actual system output. The difference signal is used as the error signal 9 for training. In order to reduce the amount of computation, it is sufficient that the learning mechanism be triggered only when the average error is constantly increasing and eventually exceeds a given threshold.

The diagnostics phase only occurs when the average error is below the threshold. This indicates that the adaptive component 8 accurately models the real system 7. Diagnostics can be performed in regular time intervals or by explicit request from a user. The derived functional model 8 is able to indicate how the system would behave under prespecified conditions of the workshop test bed. In order to provide this function, the functional model 8 is fed values for the confounding variables 4 according to the specification of the workshop tests while varying the independent variables 3 in order to study its simulated output. In the instance of fuel cell diagnostics, this can be achieved by setting the stack temperature and the differential pressure (hydrogen to air side) to a fixed value for a certain output power or by using the same exact values for input variables as previously seen under workshop conditions. On the basis of the comparison by comparator 12 between a reference curve and the estimated curve, the diagnostics module 10 can either inform the driver using a Human Machine Interface (HMI) or send the result of the analysis to a data center using wireless communication where it can, in turn, be fed back to technicians and design engineers.

A comparison of the stationary test data recorded on the workshop test bed with values estimated by the neural network which was trained with everyday driving data recorded on the same day as the workshop test is shown in FIG. 4. The same input data is fed into each test. From the location of the areas of uncertainty, as far as their size and shape, it is to be noted that there is quite an accurate agreement between the two tests. Upon interpolation of both sets of data the resulting curves are satisfactory for diagnostic purposes because having a narrow band or a single line as a reference only requires minor onboard diagnostics algorithms to determine if the current real time powertrain data provides a tolerance band indicating “satisfactory” or “healthy” conditions.

The above described onboard diagnostic enables a speed-up in the development cycle of new technologies because design engineers can be provided feedback data concerning wear, tear and failure of the monitored system in an expedited manner. Furthermore, user support and acceptance can be increased by early warning and reduced down time (predictive maintenance). Therefore, service intervals can be adjusted to actual service demand which is particular important for emerging and not yet completely mature technologies such as fuel cell cars. Additionally, the present system allows for onboard diagnostics with a significant data reduction compared to complete data recording, which is the method typically used with research fleets. Additionally, due to the automated operation, the high labor cost for manual post processing of data is significantly reduced.

The continuously created models of the powertrain in the adaptive model 8 can be transmitted over a wireless connection to a central fleet database for the purpose of observing each individual vehicle and the vehicle fleet as a whole, which is part of a statistical approach. The present system contributes to each of the goals by enabling feasible and robust on-board diagnostics systems.

The foregoing disclosure has been set forth merely to illustrate the invention and is not intended to limiting. Since modifications of the disclosed embodiments incorporating the spirit and substance of the invention may occur to persons skilled in the art, the invention should be construed to include everything within the scope of the appended claims and equivalents thereof.

Claims (32)

What is claimed is:
1. A method for on-board real-time diagnostics of a system, said method comprising the steps:
providing a reference model containing predefined operating conditions and predefined confounding variables of said system and outputting a reference characteristic;
measuring real-world operating conditions and real-world confounding variables of said system and outputting a plurality of actual system output variables, wherein the real-world confounding variables comprise variables indicating a state of an environment of the system;
providing an adaptive model input with said real-world operating conditions and said real-world confounding variables in a first phase and inputting said predefined operating conditions and said predefined confounding variables in a second phase;
providing a first comparator for comparing said plurality of actual system output variables with an output of said adaptive model;
providing feedback means for feeding the output of said first comparativecomparator directly to an input of said adaptive model during said first phase;
providing a second comparator to compare the output of said adaptive model during the second phase with said reference characteristic output of said reference model, wherein the reference characteristic comprises a characteristic output curve for the system based on the reference model, and wherein the output of the adaptive model comprises a predicted output curve for the system based on the adaptive model;
providing a diagnostics module receiving the output of said second comparator during said second phase in order to output a diagnosis of said system.
2. The method according to claim 1, further including the step of switching between said first phase and said second phase wherein said first phase is a training phase and said second phase is a diagnostics phase.
3. The method according to claim 1, wherein said reference characteristic is a series of measured response functions generated by a stationary test of said system.
4. The method according to claim 3, wherein said measured response function provides a polarization curve generated by a stationary test of a fuel cell powertrain.
5. The method according to claim 3, wherein said measured response function provide a speed/torque curve generated by a stationary test of an internal combustion engine.
6. The method according to claim 1, wherein said system is a fuel cell powertrain.
7. The method according to claim 1, wherein said real-world operating conditions and said real-world confounding variables are generated when a vehicle containing said system is being driven during normal operation.
8. The method according to claim 1, wherein said system is a mobile technical system of a vehicle.
9. An arrangement for real time diagnostics of a system, comprising:
a reference model receiving configured to receive predefined operating conditions and predefined confounding variables of said system and outputting a reference characteristic;
means for inputtingan input mechanism configured to input to said system real-world operating conditions and real-world confounding variables of said system wherein the output of said system provides actual system output variables;
an adaptive model receiving configured to receive, in a first phase, said real-world operating conditions and said real-world confounding variables and, in a second phase said pre-defined operating conditions and said predefined confounding variables to provide a first output during said first phase and a second output during said second phase;
a first comparator means for comparingconfigured to compare said actual system output variables with said first output of said adaptive model;
a feedback means receivingmechanism configured to receive an output of said first comparator means and feeding feed said output directly to said adaptive model during said first phase;
a second comparator means for comparingconfigured to compare an output of said reference model with the second output of said adaptive model during said second phase, wherein the output of said reference model comprises a characteristic output curve for the system based on said reference model, and wherein the second output of said adaptive model comprises a predicted output curve for the system based on said adaptive model;
a diagnostics module receiving configured to receive an output of said second comparator during said second phase; and
a switching means for switching mechanism configured to switch between said first and second phase.
10. The arrangement according to claim 9, wherein said first phase is a training phase and said second phase is a diagnostics phase.
11. The arrangement according to claim 9, wherein said reference characteristics are a series of measured response functions generated by a stationary test of said system.
12. The arrangement according to claim 11, wherein said measured response functions provide a polarization curve generated by a stationary test of a fuel cell powertrain.
13. The arrangement according to claim 11, wherein said measured response functions provide speed/torque curve generated by a stationary test of an internal combustion engine.
14. The arrangement according to claim 9, wherein said system is a fuel cell powertrain.
15. The arrangement according to claim 9, wherein said real-world operation conditions and said real-world confounding variables are generated from a measuring means during the normal driving operation of a vehicle containing said system.
16. A method comprising:
receiving, by a processing device, an actual-system output from a vehicle component;
adapting, by the processing device, an adaptive model in a training phase in response to a received error signal, wherein the received error signal is received at the adaptive model, wherein the received error signal is a difference between a predicted output of the adaptive model and the actual-system output, and wherein the difference is used to train the adaptive model;
generating, by the processing device, the predicted output in a diagnostic phase based on a received set of diagnostic conditions;
switching, by the processing device, between the training phase and the diagnostic phase;
when in the diagnostic phase:
comparing, by the processing device, a reference output from a reference model to the predicted output, wherein the reference output comprises a characteristic output curve for the vehicle component based on the reference model, and wherein the predicted output comprises a predicted output curve for the vehicle component based on the adaptive model; and
providing, by the processing device, an indication based on the comparison.
17. The method of claim 16 wherein the adaptive model utilizes adaptive curve fitting.
18. The method of claim 16 wherein the adaptive model utilizes a three-layer feedforward neural network.
19. The method of claim 16 wherein the adaptive model is trained only when the received error is above a threshold.
20. The method of claim 16 further comprising transmitting the adaptive model to a database.
21. The method of claim 16 wherein the switching between the training phase and the diagnostic phase is in response to a request.
22. The method of claim 16 wherein the switching between the training phase and the diagnostic phase occurs according to a schedule.
23. A real-time diagnostic device, comprising:
a vehicle component, configured to provide an actual-system output;
an adaptive model, configured to:
generate a predicted output in a training phase by adjusting the adaptive model in response to a received error signal, wherein the received error signal is received at the adaptive model, wherein the received error signal is a difference between the predicted output of the adaptive model and the actual-system output, and wherein the difference is used to train the adaptive model;
generate the predicted output in a diagnostic phase based on a received set of diagnostic conditions;
switch between the training phase and the diagnostic phase;
a reference model, configured to: generate a reference output;
a comparator, configured to:
compare the predicted output in the diagnostic phase to the reference output, wherein the reference output comprises a characteristic output curve for the vehicle component based on the reference model, and wherein the predicted output in the diagnostic phase comprises a predicted output curve for the vehicle component based on the adaptive model, and
provide an indication based on the comparison.
24. The device of claim 23 wherein the adaptive model is configured to use curve fitting.
25. The device of claim 23 wherein the adaptive model is configured to use neural networks.
26. The device of claim 23 further comprising a diagnostics module for transmitting the adaptive model to a server.
27. The method of claim 16, wherein switching between the training phase and the diagnostic phase comprises switching to the diagnostic phase only when an average error corresponding to the received error signal is below a threshold.
28. The method of claim 16, further comprising triggering the training phase only when an average error corresponding to the received error signal is increasing and exceeds a threshold.
29. The method according to claim 1, wherein the real-world confounding variables comprise at least an outside temperature.
30. The method according to claim 1, wherein the providing the adaptive model input with said predefined operating conditions and said predefined confounding variables in the second phase further comprises:
setting said predefined confounding variables to a fixed value; and
varying said predefined operating conditions.
31. The method according to claim 1, wherein during the second phase:
the characteristic output curve is further based on said predefined operating conditions and said predefined confounding variables, and
the predicted output curve is further based on said predefined operating conditions and said predefined confounding variables.
32. The device of claim 23, wherein the adaptive model is further configured to switch to the diagnostic phase only when the received error signal is below a threshold, and wherein the received error signal being below a threshold indicates that the adaptive model accurately models the actual-system output from the vehicle component.
US12291990 2004-05-28 2008-11-14 Method for simplified real-time diagnoses using adaptive modeling Active USRE45815E1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US10855315 US7136779B2 (en) 2004-05-28 2004-05-28 Method for simplified real-time diagnoses using adaptive modeling
US12291990 USRE45815E1 (en) 2004-05-28 2008-11-14 Method for simplified real-time diagnoses using adaptive modeling

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US12291990 USRE45815E1 (en) 2004-05-28 2008-11-14 Method for simplified real-time diagnoses using adaptive modeling

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US10855315 Reissue US7136779B2 (en) 2004-05-28 2004-05-28 Method for simplified real-time diagnoses using adaptive modeling

Publications (1)

Publication Number Publication Date
USRE45815E1 true USRE45815E1 (en) 2015-12-01

Family

ID=35433334

Family Applications (2)

Application Number Title Priority Date Filing Date
US10855315 Active 2024-10-19 US7136779B2 (en) 2004-05-28 2004-05-28 Method for simplified real-time diagnoses using adaptive modeling
US12291990 Active USRE45815E1 (en) 2004-05-28 2008-11-14 Method for simplified real-time diagnoses using adaptive modeling

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US10855315 Active 2024-10-19 US7136779B2 (en) 2004-05-28 2004-05-28 Method for simplified real-time diagnoses using adaptive modeling

Country Status (2)

Country Link
US (2) US7136779B2 (en)
DE (1) DE102005020821A1 (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2354837C (en) * 2000-08-11 2005-01-04 Honda Giken Kogyo Kabushiki Kaisha Simulator for automatic vehicle transmission controllers
US7136779B2 (en) * 2004-05-28 2006-11-14 Daimlerchrysler Ag Method for simplified real-time diagnoses using adaptive modeling
US7925479B2 (en) * 2007-07-20 2011-04-12 Honda Motor Co., Ltd. Efficient process for evaluating engine cooling airflow performance
DE102009059137A1 (en) 2009-12-19 2010-07-29 Daimler Ag Diagnostic method for on-board determination of wear state of fuel cell system in motor vehicle, involves using values and measuring values from operating region, which comprises reduced model accuracy, for adaptation of model parameter
CA2823072A1 (en) * 2011-01-03 2012-07-12 650340 N.B. Ltd. Systems and methods for extraction and telemetry of vehicle operational data from an internal automotive network
CA2827575C (en) * 2011-02-18 2018-01-02 650340 N.B. Ltd. Systems and methods for extraction of vehicle operational data and sharing data with authorized computer networks
FI20116256A (en) * 2011-12-09 2013-06-10 Waertsilae Finland Oy Method and arrangement for detecting solid oxide cell operating conditions
FI20116257A (en) * 2011-12-09 2013-06-10 Waertsilae Finland Oy Method and arrangement for diagnosis of a solid oxide cell operating conditions
DE102013017059A1 (en) 2013-10-15 2014-07-24 Daimler Ag Method for detecting wear condition of accumulator in motor vehicle, involves determining wearing condition by model, in which virtual loading profiles are provided with operating value

Citations (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4663703A (en) * 1985-10-02 1987-05-05 Westinghouse Electric Corp. Predictive model reference adaptive controller
US5024801A (en) * 1989-05-01 1991-06-18 Westinghouse Electric Corp. Reactor core model update system
US5247445A (en) * 1989-09-06 1993-09-21 Honda Giken Kogyo Kabushiki Kaisha Control unit of an internal combustion engine control unit utilizing a neural network to reduce deviations between exhaust gas constituents and predetermined values
US5355749A (en) * 1991-12-20 1994-10-18 Hitachi, Ltd. Control apparatus and control method for motor drive vehicle
US5682317A (en) * 1993-08-05 1997-10-28 Pavilion Technologies, Inc. Virtual emissions monitor for automobile and associated control system
US5898282A (en) * 1996-08-02 1999-04-27 B.C. Research Inc. Control system for a hybrid vehicle
US6085183A (en) * 1995-03-09 2000-07-04 Siemens Aktiengesellschaft Intelligent computerized control system
US6108648A (en) * 1997-07-18 2000-08-22 Informix Software, Inc. Optimizer with neural network estimator
US20020092988A1 (en) * 1998-10-30 2002-07-18 Didomenico John Multilane remote sensing device
US20030014131A1 (en) * 1996-05-06 2003-01-16 Havener John P. Method for optimizing a plant with multiple inputs
US6574613B1 (en) * 1998-02-27 2003-06-03 Jorge Moreno-Barragan System and method for diagnosing jet engine conditions
US6609051B2 (en) 2001-09-10 2003-08-19 Daimlerchrysler Ag Method and system for condition monitoring of vehicles
US6625539B1 (en) * 2002-10-22 2003-09-23 Electricab Taxi Company Range prediction in fleet management of electric and fuel-cell vehicles
US6760716B1 (en) * 2000-06-08 2004-07-06 Fisher-Rosemount Systems, Inc. Adaptive predictive model in a process control system
US6792341B2 (en) * 2002-10-23 2004-09-14 Ford Motor Company Method and system for controlling power distribution in a hybrid fuel cell vehicle
US6837115B2 (en) * 2001-08-24 2005-01-04 Symyx Technologies, Inc. High throughput mechanical rapid serial property testing of materials libraries
US6847881B2 (en) * 2001-09-05 2005-01-25 Siemens Aktiengesellschaft Method and device for controlling piezo-driven fuel injection valves
US6893756B2 (en) * 2002-04-30 2005-05-17 General Motors Corporation Lambda sensing with a fuel cell stack
US20050119842A1 (en) * 2003-12-02 2005-06-02 Clingerman Bruce J. Load following algorithm for a fuel cell based system
US6909959B2 (en) * 2003-03-07 2005-06-21 Stephen James Hallowell Torque distribution systems and methods for wheeled vehicles
US7035834B2 (en) * 2002-05-15 2006-04-25 Caterpillar Inc. Engine control system using a cascaded neural network
US7136860B2 (en) * 2000-02-14 2006-11-14 Overture Services, Inc. System and method to determine the validity of an interaction on a network
US7136779B2 (en) * 2004-05-28 2006-11-14 Daimlerchrysler Ag Method for simplified real-time diagnoses using adaptive modeling
US7183962B1 (en) * 2004-05-17 2007-02-27 Marvell International Ltd. Low power asynchronous data converter
US7308322B1 (en) * 1998-09-29 2007-12-11 Rockwell Automation Technologies, Inc. Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis
US20090073021A1 (en) * 2007-09-17 2009-03-19 Samsung Electronics Co., Ltd. Cascade comparator and control method thereof
US7539597B2 (en) * 2001-04-10 2009-05-26 Smartsignal Corporation Diagnostic systems and methods for predictive condition monitoring
US7613663B1 (en) * 2002-09-30 2009-11-03 Michael Lamport Commons Intelligent control with hierarchal stacked neural networks
US7860682B2 (en) * 2003-10-31 2010-12-28 Seebyte, Ltd. Intelligent integrated diagnostics
US8260733B2 (en) * 1999-02-02 2012-09-04 Garbortese Holdings, Llc Neural network system and method for controlling information output based on user feedback

Patent Citations (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4663703A (en) * 1985-10-02 1987-05-05 Westinghouse Electric Corp. Predictive model reference adaptive controller
US5024801A (en) * 1989-05-01 1991-06-18 Westinghouse Electric Corp. Reactor core model update system
US5247445A (en) * 1989-09-06 1993-09-21 Honda Giken Kogyo Kabushiki Kaisha Control unit of an internal combustion engine control unit utilizing a neural network to reduce deviations between exhaust gas constituents and predetermined values
US5355749A (en) * 1991-12-20 1994-10-18 Hitachi, Ltd. Control apparatus and control method for motor drive vehicle
US5682317A (en) * 1993-08-05 1997-10-28 Pavilion Technologies, Inc. Virtual emissions monitor for automobile and associated control system
US6085183A (en) * 1995-03-09 2000-07-04 Siemens Aktiengesellschaft Intelligent computerized control system
US20030014131A1 (en) * 1996-05-06 2003-01-16 Havener John P. Method for optimizing a plant with multiple inputs
US5898282A (en) * 1996-08-02 1999-04-27 B.C. Research Inc. Control system for a hybrid vehicle
US6108648A (en) * 1997-07-18 2000-08-22 Informix Software, Inc. Optimizer with neural network estimator
US6574613B1 (en) * 1998-02-27 2003-06-03 Jorge Moreno-Barragan System and method for diagnosing jet engine conditions
US7308322B1 (en) * 1998-09-29 2007-12-11 Rockwell Automation Technologies, Inc. Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis
US20020092988A1 (en) * 1998-10-30 2002-07-18 Didomenico John Multilane remote sensing device
US8260733B2 (en) * 1999-02-02 2012-09-04 Garbortese Holdings, Llc Neural network system and method for controlling information output based on user feedback
US7136860B2 (en) * 2000-02-14 2006-11-14 Overture Services, Inc. System and method to determine the validity of an interaction on a network
US6760716B1 (en) * 2000-06-08 2004-07-06 Fisher-Rosemount Systems, Inc. Adaptive predictive model in a process control system
US7539597B2 (en) * 2001-04-10 2009-05-26 Smartsignal Corporation Diagnostic systems and methods for predictive condition monitoring
US6837115B2 (en) * 2001-08-24 2005-01-04 Symyx Technologies, Inc. High throughput mechanical rapid serial property testing of materials libraries
US6847881B2 (en) * 2001-09-05 2005-01-25 Siemens Aktiengesellschaft Method and device for controlling piezo-driven fuel injection valves
US6609051B2 (en) 2001-09-10 2003-08-19 Daimlerchrysler Ag Method and system for condition monitoring of vehicles
US6893756B2 (en) * 2002-04-30 2005-05-17 General Motors Corporation Lambda sensing with a fuel cell stack
US7035834B2 (en) * 2002-05-15 2006-04-25 Caterpillar Inc. Engine control system using a cascaded neural network
US7613663B1 (en) * 2002-09-30 2009-11-03 Michael Lamport Commons Intelligent control with hierarchal stacked neural networks
US6625539B1 (en) * 2002-10-22 2003-09-23 Electricab Taxi Company Range prediction in fleet management of electric and fuel-cell vehicles
US6792341B2 (en) * 2002-10-23 2004-09-14 Ford Motor Company Method and system for controlling power distribution in a hybrid fuel cell vehicle
US6909959B2 (en) * 2003-03-07 2005-06-21 Stephen James Hallowell Torque distribution systems and methods for wheeled vehicles
US7860682B2 (en) * 2003-10-31 2010-12-28 Seebyte, Ltd. Intelligent integrated diagnostics
US20050119842A1 (en) * 2003-12-02 2005-06-02 Clingerman Bruce J. Load following algorithm for a fuel cell based system
US7183962B1 (en) * 2004-05-17 2007-02-27 Marvell International Ltd. Low power asynchronous data converter
US7136779B2 (en) * 2004-05-28 2006-11-14 Daimlerchrysler Ag Method for simplified real-time diagnoses using adaptive modeling
US20090073021A1 (en) * 2007-09-17 2009-03-19 Samsung Electronics Co., Ltd. Cascade comparator and control method thereof

Also Published As

Publication number Publication date Type
DE102005020821A1 (en) 2005-12-22 application
US20050278146A1 (en) 2005-12-15 application
US7136779B2 (en) 2006-11-14 grant

Similar Documents

Publication Publication Date Title
Frank et al. Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis
US7415389B2 (en) Calibration of engine control systems
Chiang et al. Fault detection and diagnosis in industrial systems
US20070005311A1 (en) Automated model configuration and deployment system for equipment health monitoring
US6609051B2 (en) Method and system for condition monitoring of vehicles
US20060230313A1 (en) Diagnostic and prognostic method and system
US20080140352A1 (en) System and method for equipment life estimation
Byington et al. Data-driven neural network methodology to remaining life predictions for aircraft actuator components
US6748341B2 (en) Method and device for machinery diagnostics and prognostics
US20050261837A1 (en) Kernel-based system and method for estimation-based equipment condition monitoring
Wang et al. Fault prognostics using dynamic wavelet neural networks
US20110238258A1 (en) Event-driven fault diagnosis framework for automotive systems
US6226760B1 (en) Method and apparatus for detecting faults
US7756678B2 (en) System and method for advanced condition monitoring of an asset system
US6240343B1 (en) Apparatus and method for diagnosing an engine using computer based models in combination with a neural network
Peng et al. A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction
US6836708B2 (en) Monitoring of vehicle health based on historical information
US7496798B2 (en) Data-centric monitoring method
Kalgren et al. Defining PHM, a lexical evolution of maintenance and logistics
US7457785B1 (en) Method and apparatus to predict the remaining service life of an operating system
Rizos et al. Friction identification based upon the LuGre and Maxwell slip models
Yung et al. Local sensor validation
US7260501B2 (en) Intelligent model-based diagnostics for system monitoring, diagnosis and maintenance
Kwan et al. A novel approach to fault diagnostics and prognostics
Sampath A discrete event systems approach to failure diagnosis

Legal Events

Date Code Title Description
AS Assignment

Owner name: RAMSLE TECHNOLOGY GROUP GMBH, LLC, DELAWARE

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DAIMLER AG;REEL/FRAME:025550/0060

Effective date: 20080727

AS Assignment

Owner name: OL SECURITY LIMITED LIABILITY COMPANY, DELAWARE

Free format text: MERGER;ASSIGNOR:RAMSLE TECHNOLOGY GROUP GMBH, LLC;REEL/FRAME:037358/0685

Effective date: 20150826

MAFP

Free format text: PAYMENT OF MAINTENANCE FEE, 12TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1553)

Year of fee payment: 12