GB2501291A - Diagnostic system with predicted problem cause feedback - Google Patents

Diagnostic system with predicted problem cause feedback Download PDF

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Publication number
GB2501291A
GB2501291A GB1206844.1A GB201206844A GB2501291A GB 2501291 A GB2501291 A GB 2501291A GB 201206844 A GB201206844 A GB 201206844A GB 2501291 A GB2501291 A GB 2501291A
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United Kingdom
Prior art keywords
event
data
fault
signature
machine
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GB1206844.1A
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GB201206844D0 (en
Inventor
Ken Scott
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PROJECT VANGUARD Ltd
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PROJECT VANGUARD Ltd
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Priority to GB1206844.1A priority Critical patent/GB2501291A/en
Publication of GB201206844D0 publication Critical patent/GB201206844D0/en
Priority to PCT/GB2013/051001 priority patent/WO2013156791A1/en
Publication of GB2501291A publication Critical patent/GB2501291A/en
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    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C3/00Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00Registering or indicating the working of vehicles
    • G07C5/008Registering or indicating the working of vehicles communicating information to a remotely located station
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00Registering or indicating the working of vehicles
    • G07C5/08Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808Diagnosing performance data
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00Registering or indicating the working of vehicles
    • G07C5/08Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0816Indicating performance data, e.g. occurrence of a malfunction
    • GPHYSICS
    • G07CHECKING-DEVICES
    • G07CTIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00Registering or indicating the working of vehicles
    • G07C5/08Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0841Registering performance data

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

A machine diagnostic system processes data from one or more machines to identify events satisfying predetermined event conditions indicative of faulty operation, and for each identified event generates an event signature using data associated with the event. The event signatures are then analysed to generate data identifying a prediction of the cause of a fault. The generated fault prediction data may be output to a machine operator to take appropriate action. In an embodiment, each possible event signature is mapped to corresponding fault prediction data in a fault prediction database. Preferably, fault correction data indicating an actual cause of the faulty operation and successful remedial action taken for an event is received by the machine diagnostic system and the fault correction data for the event signature corresponding to the event is updated to take account of the received fault correction data. The machine is preferably a vehicle which collects operational data and transmits the collected operational data to a remote machine diagnostic server which completes the diagnostic analysis and communicates with maintenance workshop servers, vehicle operator servers and back-up servers.

Description

Machine Diagnostic System and Components Thereof This invention concerns a system for diagnosing a fault in a machine. The invention has particular, but not exclusive, relevance to the diagnosis of a fault in a vehicle.
One application for a diagnostic system in accordance with the invention is to the management of a fleet of vehicles by a fleet operator. For example, the fleet operator may be a courier company operating a fleet of vans. Mternatively, the fleet operator may be a taxi company operating a fleet of taxis. It is known to supply information from the fleet of vehicles to the fleet operator to improve the utilisation of the fleet of vehicles, and over recent years the nature of the information provided to the fleet operator has increased.
Initially, a GPS device was fitted to each vehicle in the fleet to allow the movement of the vehicles to be monitored. Subsequently, the location information provided by the GPS devices was integrated into a scheduling service to improve scheduling of the use of the fleet of vehicles. More recently, an interface to the vehicle diagnostic network was added to enable information about vehicle and driver behaviour to be gathered while the vehicle is in use.
providing data relating to fuel consumption and carbon emissions of the vehicle.
Although fault codes produced by the on-board diagnostic network (OBD) in a vehicle can identify components of the vehicle which are not functioning properly, the cause of the fault is not provided and as such the necessary action to correct the fault can be difficult to ascertain. The present invention addresses this problem.
According to the present invention, there is provided a machine diagnostic system which processes data from one or more machines to identify events satisfying predetermined event conditions indicative of faulty operation, and for each identified event generates an event signature using data associated with the event. The event signatures are then analysed 2.5 to generate data identifying a prediction of the cause of a fault. The generated fault prediction data may be output to a machine operator to take appropriate action.
In an embodiment, each possible event signature is mapped to corresponding fault prediction data in a fault prediction database. Preferably, fault correction data indicating successful remedial action taken for an event is received by the machine diagnostic system 5:1' and the fault correction data for the event signature corresponding to the event is updated to take account of the received fault correction data.
An exemplary embodiment of the invention will now be described with reference to the attached figures, in which: Figure 1 schematicaJly shows the main components of a machine diagnostic system according to the invention; Figure 2 schematically shows the main components of a diagnostic server forming part of the machine diagnostic system illustrated in Figure 1; Figure 3 schematically shows client data stored in a client database illustrated in Figure 2; Figure 4 schematically illustrates the processing of raw diagnostic data from a machine by the diagnostic server of Figure 2; and Figure 5 schematically shows the processing of fault correction data by the diagnostic server of Figure 2.
System Overview In the exemplary embodiment of the invention, a diagnostic service operator operates a diagnostic server I which performs vehicle diagnosis services for a plurality of vehicle operators. Each of the vehicle operators has a vehicle operator server 3 (only one of which is shown in Figure 1 for ease of illustration) and operates a fleet of vehicles 5 (only a single vehicle 5 being shown in Figure i for ease of illustration). A back-up server 7 is also provided for backing up data stored in the diagnostic server I in case of damage to the diagnostic server I. The vehicle 5 indudes a driver communication device 9 which is 22 wirelessly connected, either directly or via a wireless communication network (for example a public land mobile network PLMN)), with the vehicle operator server 3 to allow the vehicle operator to communicate with the driver of the vehicle 5.
The vehicles 5 are serviced in vehicle maintenance workshops, with each vehicle maintenance workshop having a vehicle maintenance workshop server 11 (only one of which 2.5 is shown in Figure 1 for ease of illustration). The diagnostic server 1, the back-up server 7, the vehicle operator servers 3 and the vehicle maintenance workshop servers II are all connected to each other via the Internet 13. Dedicated connections (shown by dotted lines in Figure i) may be provided in addition to, or instead of. the Internet connections between (i) the diagnostic server 1 and the back-up server 7, (ii) the diagnostic server 1 and one or more 5:1' of the vehicle operator servers 3, and (iii) each vehicle operator server 3 and one or more vehicle maintenance workshop servers II.
As will be described in more detail hereafter, in accordance with the invention, raw diagnostic data from the vehicle 5 is communicated to the diagnostic server 1, which analyses the data to identify fault events and then sends fault prediction data for the identified fault events to the vehicle operator server 3 associated with the vehicle 5, which in turn forwards the fault prediction data to the vehicle maintenance workshop server 11 at the vehicle maintenance workshop where the vehicle 5 is to be serviced. The vehicle maintenance workshop uses the fault prediction data to assist servicing, and then sends, via the vehicle operator server 3, fault correction data for each fault event back to the diagnostic server 1.
The fault correction data is then used by the diagnostic server I to improve the accuracy of the fault prediction data produced for subsequent fault events, hi other words, the diagnostic server I learns through experience the most likely causes of fault events.
As shown in Figure 1, vehicle 5 indudes a plurality of electronic control units interconnected by a bus 15, typically a CAN bus. As shown, the electronic control units typically include an engine control unit 17, a transmission control unit 19 and a traction control system 2i. A driver interface 23, for example a dashboard display having an array of malfunction indicator lights, is also connected to the bus 15. Those skilled in the art will appreciate that many other electronic control units may conventionally be attached to the bus 15, for example an airbag deployment system and/or a cruise control system. An OBD port is also connected, via an OBD interface unit 27, to the bus 15.
In a conventional manner, the electronic control units monitor the operation of the vehicle 5, including processing parametric data and, if certain conditions are satisfied, 2 generating either permanent fault codes or temporary fault codes. A permanent fault code will typically result in the driver interface 23 presenting a fault warning to the driver, for example by activating a malfunction indicator light. A temporary fault code is stored by an electronic control unit for internal reference. For example, an electronic control unit may monitor the frequency of temporary fault codes and issue a permanent fault code if that 2.5 frequency exceeds a predetermined amount.
In this embodiment, a telematics device 29 is connected to the OBD port 23. The telematics device 29 probes the electronic control units connected to the bus 15 for data, logs the collated data and forwards the collated data to the diagnostic server 1. In particular, the telematics device 29 collates the generated permanent fault codes and temporary fault codes, :1' and also collects parametric data in real time as the vehicle 5 is driven. This parametric data may indude, for example, oil temperature, RPM, Air Intake Temperature, Mass Air Flow, Engine Load, Throttle Position. The telematics device 29 also includes a Global Positioning System (UPS) device (not shown) which provides both location (longitude and latitude) data and time data.
In this embodiment, the telematics device 29 can transmit raw telematic data to the diagnostic server 1 in various ways. V/bile the vehicle 5 is in operation, the telematics device s 29 transmits the telematic data wirelessly, using antenna 33, to the diagnostic server 1.
Although the vehicle 5 may transmit the telematic data wirelessly direcfly to the diagnostic server I, typically the vehicle 5 transmits to the diagnostic server I via a PLMN, not shown in Figure 1. The telematics device 29 may also be physically connected to a personal computer 31, for example by a USB cable, to allow the telematic data to be transmitted via the Internet 13 to the diagnostic server 1. Further, telematic data maybe transferred from the telematics device 29 to a portable memory device, such as a IJSB drive or a hard disk, and the portable memory device can be physically transported to the diagnostic server 1 where the telematic data is downloaded.
As shown in Figure 2, the diagnostic server I includes the following inputloutput devices for receiving and transmitting data: -a radio transceiver 41 for processing radio signals detected by an antenna 43 and transmitting radio signals using the antenna 43; -an Internet Transceiver 45 for receiving and sending IF signaJs 47; -a network transceiver 49 for receiving and sending signals 51 over a network such as a Local Area Network (LAN); and -a hard disk reader/writer 53 for reading data from or writing data onto a memory device 55.
The radio transceiver 41, the Internet transceiver 45, the network transceiver 51 and the hard disk reader/wnter 53 are all connected to a bus 57. Also connected to the bus 57 are 2.5 a processor 59, a clock 61 and memory 63. It will be appreciated that, in a conventional manner, the memory 63 may involve several memory devices having different memory characteristics. With respect to hardware, the diagnostic server 1 may be implemented by a conventional network server.
As schematically shown in Figure 2, the memory 63 has program storage memory 65, 5:1' data storage memory 67 and working memory 69. Although represented as separate memory regions in Figure 2 for ease of illustration, typically these memory regions will not be formed by contiguous blocks of memory addresses.
The program storage memory 65 stores a Master_Control_Routine 71, which controls the operation of the diagnostic server 1. As will be described in more detail hereatler, in response to the receipt of telematic data from a vehicle 5, the Master_Control_Routine 71 initiates a Process_Raw_Data sub-routine 73. Further, in response to receiving fault s correction data from a vehicle maintenance workshop server II, the Master_Control_Routine 71 initiates a Process_PC_Data sub-routine 75.
The data storage memory 67 stores a client database 77, raw telematic data 79, a datastream database 81, a failure mode database 83 and a fault effect signature database 85.
The client database 77 stores data for each of the vehicle operators. As schematicaJly shown in Figure 3, the data 91 stored for a vehide operator includes: -device rules 93 providing device details for each vehide 5 owned by the vehicle operator, including an identification number and the format of transmitted telematic data; -event rules 95 specifying data indicative of conditions that the vehicle operator wishes to monitor, an occurrence of such conditions being hereafter referred to as an event; report rules 97 specifying specific telematic data which the vehicle operator wishes reported; -warning rules 99 specifying events which the vehicle operator wishes to be warned about; and -prediction rules 101 specifying events for which the vehicle operator wishes to be 2 provided with information predicting the likely cause of the event.
It will be appreciated that the client database may be stored in the form of a relationaJ database to facilitate the accuracy of data entry and data updating.
The datastream database 81 stores the received telematic data after it has been decoded into a standardised format. The fault effect signature database 85 stores a fault effect 2.5 signature for each event in association with a unique event identifier, the fault effect signature being data associated with the event. The failure mode database 83 stores data indicating, for each possible fault effect signature, the likely possible causes of the corresponding event.
The processing of received telematic data will now be discussed with reference to Figure 4. As mentioned above, receipt of the telematic data triggers the execution of the 3:1' Process_Raw_Data sub-routine 73 which first stores, at Sl. the received telematic data in the raw data memory region 79 and the back-up server 7. By storing all received telematic data in the back-up server 7, the telematic data may be recovered in case of damage to the diagnostic server 1.
In this embodiment, the diagnostic server 1 is not reliant on the received data conforming with a particular format, but rather stores the format of the received data for each device in the device rules 93. This facilitates the use of the diagnostic server 1 with telematics devices 29 produced by different manufacturers. In addition, the format in which s the telematics data is transmitted may be dependent on the medium by which it is communicated to the diagnostic server 1 (e.g. by radio waves, the Internet or a memory card).
The diagnostic server I decodes, at S3, the telematic data into a common format using the device rules 93. This decoded data is then stored, at S5, in the datastream database 81.
Typically, each portion (referred to hereafter as a message) of decoded data includes: :1' * Vehicle Identification * UPS data (including latitude/longitude co-ordinates and timestamp) * Acceleration/Dec&eration Data * Message Receipt Timestamp * Message Seq uence Number -5 S Permanent Fault Codes * Temporary Fault Codes * Parametric Data The diagnostic server I generates, at S7, a report to the vehicle operator based on the message data stored in the datastream database 81. The message data will typically include 22 telematic data such as fuel consumption, odometer, vehicle battery charge, RPM, vehicle speed, coolant temperature and driver behaviour. The vehicle operator does not necessarily wish all of this information included in the report, and accordiny the report data is generated using the report rules 97 specified by the vehicle operator.
In accordance with the present invention, the diagnostic server I identifies, at S9, 2.5 events occurring within received telemadc data using the event rules 95. Each event rule specifies conditions in one message, or a sequence of messages, which if satisfied indicate an event that is indicative of a fault in the vehicle 5 and requiring of investigation has occurred.
Such event rules may specify conditions to be satisfied relating to fault codes, both permanent and temporary, and parametric data. For example, an event rule could specify one or more of 33 the following: * A permanent fault code is detected * A temporary fault code is detected * A parametric data value is out of tolerance Following identification of an event, the diagnostic server 1 generates, at Si 1, a fault effect signature for the event and stores the fault effect signature in the fault effect signature database 85 in association with a unique event identifier. The fault effect signature provides data representative of the event, and more particularly provides data showing the effect that a particular fault has on the fault code(s) and parametric data. In one form, the fault effect signature could contain all the telematic data associated with an event. However, in a preferred form, the faull effect signature will indude a compressed form of the telematic data associated with an event. It will be appreciated that many data processing techniques are available to compress the telematic data so that each fault effect signature remains representative of a respective particular fault.
In this embodiment, the faull effect signature includes a portion which is representative of the vehicle 5 and a portion which is representative of the raw data from the vehicle 5. In this way, the same fault in identical vehicles will result in the same fault effect signature.
Following generation of the fault effect signature, the diagnostic server 1 generates, at S13, warning data to advise the vehicle operator of the fault. The vehicle operator may not wish to be advised of every fault, and accordingly the diagnostic server I consults the warning rules 99 specified by the vehicle operator to determine the events for which warnings are to be issued. In this embodiment, the warning data includes the unique event identifier of each 22 event for which a warning is issued.
The diagnostic server 1 also predicts, at Si5. the cause of the fault using the failure mode database 83. As will be discussed hereafter, in this embodiment the failure mode database 83 logs reported causes of failure associated with every possible fault effect signature, and then subsequently uses this logged data to predict the likely cause of a fault for 2.5 a newly generated fault effect signature. For example, for a particular fault effect signature the failure mode database 83 may have logged that previously, the fault associated with the fault effect signature had been caused by reason A' seven times, reason B' two times and reason C' one time, and accordingly will predict that there is a 70% chance that the fault was caused by reason A', a 20% chance that the fault was caused by reason B' and a 10% chance 2:1' that the fault was caused by reason C'.
Following the determination of lik&y causes of a fault, the diagnostic server I generates, at S17, prediction data to advise the vehicle operator of the predicted cause of a fault. The vehicle operator may not wish to receive all the prediction data, but rather may prefer oniy to be advised of the most likely cause or any causes which are above a certain level of likelihood. Accordingly, the diagnostic server I consults the prediction rules 101 specified by the vehicle operator to determine the exact prediction data to be sent to the vehicle operator. For each event for which prediction data is generated, the prediction data s includes the unique event identifier for that event in association with data identifying the predicted cause of that event.
The diagnostic server I sends the report data, the warning data and the prediction data to the vehicle operator server 3. Following receipt of the warning data, the vehicle operator may decide to submit a vehicle 5 for which a warning is issued to a vehicle maintenance workshop for servicing. The vehicle operator server 3 sends a message conveying the warning data and the corresponding prediction data for the vehide 5 to the vehicle maintenance workshop server 11 for the vehicle maintenance workshop handling the servicing. The prediction data facilitates servicing and repair of the vehicle by suggesting the most likely cause or causes of a fault. In this way, the vehicle may be serviced and repaired in less time and at less expense.
When the vehicle maintenance workshop has determined the actual cause of the fault, the vehicle maintenance workshop server II sends, either directly or via the vehicle operator server 3, fault correction data indicating the actual cause of the fault to the diagnostic server 1.
In particular, the fault correction data includes for each corrected event, the unique event 2 identifier and data indicating the cause of failure for that event.
As shown in Figure 5, following receipt of the fault correction data, the diagnostic server I updates, at S21, the entry in the failure mode database 83 for the corresponding fault effect signature with the actual cause of the fault. The diagnostic server 1 identifies the colTect entry in the failure mode database by referencing the fault effect signature 2.5 colTesponding to the unique event identifier conveyed in the fault correction data. In this way, the diagnostic server I learns through experience the likely causes associated with every possible fault effect signature.
As discussed above, the arrangement of the present embodiment has several advantages. One advantage is the ability to provide early warning of faults, and predictions of 3:1' the causes of the faults, in real time while the vehicle is in operation. It will also be appreciated that, over time, the fault prediction data will become more and more accurate as a result of experiential learning by the diagnostic server 1.
MODIFICATIONS AND FURTHER EMBODIMENT
In the illustrated embodiment, the diagnostic server I is used to analyse the operation of a vehicle 5. It will be appreciated that the diagnostic server 1 could alternatively be used to anaJyse the operation of different types of machine. For the avoidance of doubt, the term s machine covers any form of mechanical., electrical and/or chemical. apparatus or system having means for monitoring the operation of the machine and generating raw data indicative of that operation, and means for transmitting the raw data to a diagnostic server I. Other examples of machines suitable for the present invention include elecfric vehicles, hybnd vehicles, alternative fuel vehicles. seacraft, aircraft, agricultural and construction vehicles, military vehicles, industrial and robotic machinery, and power generators.
The invention requires that fault prediction data is determined for each fault effect signature. In the illusfrated embodiment, this is achieved by a failure mode database which maps each fault effect signature to colTesponding fault prediction data, and the failure mode database is updated as fault correction data is received. Alternatively, the diagnostic server I may use a neural network to generate the fault prediction data through experiential learning.
The embodiment described with reference to the drawings involves peitorming process instructions defined by a computer program using some form of processing apparatus.
The invention therefore aJso extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the 2 form of source code, object code, a code intermediate to source code and object code such as in partially compiled form, or in any other form suitable for using in the implementation of the processes according to the invention.
The carrier may be any entity or device capable of carrying the program. For example, the carrier may comprise a storage medium, such as a ROM. for example a CD- 2.5 ROM or a semiconductor ROM, or a magnetic recording medium, for example a floppy disc or a hard disc, or an optical recording medium. Further, the carrier may be a transmissible carrier such as an electronic or optical signal which may be conveyed via electrical or optical cable or by radio or other means.
The carrier may be an integrated circuit in which the program is embedded, the :1' integrated circuit being adapted for performing, or for use in the performance of, the relevant processes.
Although the invention may be implemented by software, it will be appreciated that alternatively the invention could be implemented by hardware devices or a combination of hardware devices and software.

Claims (1)

  1. CLAIMS1. A diagnostic apparatus comprising: means for receiving raw data from a machine; means for processing the received raw data to identify events in the raw data, each event corresponding to the received raw data satisfying a predetermined event condition indicative of faulty operation of the machine; means for generating an event signature for each event using raw data associated with the event, the event signature for an event being characteristic of the corresponding faulty operation; and means for determining fault prediction data corresponding to each event signature, the fault prediction data identifying one or more predicted causes of said corresponding faulty operation for the event.
    2. A diagnostic apparatus according to claim 1, wherein the determining means comprises a database storing fault prediction data for each possible event signature.3. A diagnostic server according to claim 2, further comprising: means for receiving fault correction data for a generated event signature, the fault 22 colTection data indicating an actual cause of the faulty operation associated with the generated event signature; and means for updating the fault correction data for the generated fault signature to take account of the received fault colTection data.2.5 4. A diagnostic system comprising a machine and a diagnostic apparatus according to any preceding claim, wherein the machine comprises: means for monitoring the machine during operation and generating said raw data; and means for transmitting said raw data to the diagnostic apparatus.
    3:1' 5. A diagnostic system according to any preceding claim, wherein the raw data comprises one or more of fault codes and parameter data.
    II
    I
    6. A diagnostic system according to any of claims 4 to 6, wherein the machine comprises a vehicle.
    7. A method of operation of a diagnostic apparatus, the method comprising: receiving raw data from a machine; processing the received raw data to identify events in the raw data, each event corresponding to the received raw data satisfying a predetermined event condition indicative of faulty operation of the machine; generating an event signature for each event using raw data associated with the event, the event signature for an event being characteristic of the corresponding faulty operation; and determining fault prediction data corresponding to each event signature, the fault prediction data identifying one or more predicted causes of said corresponding faulty operation for the event.
    8. A method according to claim 7. wherein said determining comprises retrieving fault prediction data for the event signature from a database storing fault prediction data for each possible event signature.
    9. A method according to claim 8, further comprising: 2 receiving fault correction data for a generated event signature, the fault correction data indicating an actuaJ cause of the faulty operation associated with the generated event signature; and updating the fault correction data for the generated fault signature to take account of the received fault correction data. 2.5
    10. A method according to any of claims 7 to, wherein the raw data is generated during operation of the machine.
    11. A computer program for programming a programmable device to perform a method as claimed in any of claims 7 to 10.
GB1206844.1A 2012-04-19 2012-04-19 Diagnostic system with predicted problem cause feedback Withdrawn GB2501291A (en)

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PCT/GB2013/051001 WO2013156791A1 (en) 2012-04-19 2013-04-19 Machine analytic system and components thereof

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