WO2014117245A1 - Prévision de défaillances et de problèmes de performances - Google Patents

Prévision de défaillances et de problèmes de performances Download PDF

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Publication number
WO2014117245A1
WO2014117245A1 PCT/CA2013/000499 CA2013000499W WO2014117245A1 WO 2014117245 A1 WO2014117245 A1 WO 2014117245A1 CA 2013000499 W CA2013000499 W CA 2013000499W WO 2014117245 A1 WO2014117245 A1 WO 2014117245A1
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WO
WIPO (PCT)
Prior art keywords
data
risk
performance issue
event data
gaming
Prior art date
Application number
PCT/CA2013/000499
Other languages
English (en)
Inventor
Bharat Gadher
Fayez Idris
Original Assignee
Spielo International Canada Ulc
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
Priority claimed from US13/755,718 external-priority patent/US9084937B2/en
Application filed by Spielo International Canada Ulc filed Critical Spielo International Canada Ulc
Publication of WO2014117245A1 publication Critical patent/WO2014117245A1/fr

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Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
    • G07F17/32—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for games, toys, sports, or amusements
    • G07F17/3225—Data transfer within a gaming system, e.g. data sent between gaming machines and users
    • G07F17/3232—Data transfer within a gaming system, e.g. data sent between gaming machines and users wherein the operator is informed
    • G07F17/3234—Data transfer within a gaming system, e.g. data sent between gaming machines and users wherein the operator is informed about the performance of a gaming system, e.g. revenue, diagnosis of the gaming system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance

Definitions

  • the disclosure relates to systems and methods for predicting failures or performance issues that may be configured for gaming machines and systems.
  • Maintenance operations on gaming devices are typically reactive, where repair occurs after a fault or problem has arisen.
  • a gaming device is down and remains that way until the repair is accomplished.
  • a problem is encountered, usually, by a player who informs a human operator at the retailer, e.g., a casino or lottery ticket agent.
  • the operator may call a service provider, and turn off or take the gaming device out of service until a technician arrives to service or repair the gaming device.
  • new systems and methods for predictive diagnostic and prognostic maintenance systems are provided.
  • the systems and methods may be configured for use with networked gaming machines.
  • the systems and methods may operate in real time and may detect and analyze data representing various indicators of machine performance or a current or future decrease in machine performance. The data may represent or be used to identify necessary or recommended repair, maintenance or other performance issues.
  • systems and methods are disclosed for automated analysis of data regarding machine operation and generation of rules related to predicting the future performance, repair and maintenance needs of machines.
  • the systems and methods may automatically update existing rules related to machine performance, repair and maintenance predictions, or needs.
  • sensors on the machines and related software monitor, accumulate, store and share information for efficient maintenance and repair of gaming systems. Diagnostic and prognostic tests may be run and the results prioritized to identify and store data related to predictions or expectations of future problems or requirement for machine maintenance or repair.
  • Individual machines may send event codes and sensed data corresponding to machine status, problematic issues, machine or system errors or other indicators related to the machines or their operation.
  • the sensed data may be collected and stored in a database.
  • the system may analyze the event codes and sensed data received during a time-window from a particular machine and diagnose the machine.
  • the system may also examine the received data to provide prognosis of the machine's status over a defined time-horizon. In this way, the system may provide a prediction of future machine performance as well as future needs for machine repair, maintenance or upgrades.
  • the system can be dynamic and can adapt to changes in the expectations for future machine performance and future needs for machine or software repair, maintenance or upgrades, for example based on analysis of machine or software performance data.
  • methods and systems are provided to analyze historical or ongoing (real time) machine performance data (for example, from a collection of machines) and determine a set of rules that may be used in an analysis of machine data to predict future machine performance and future needs for machine repair, maintenance or upgrades.
  • the system and methods may provide feedback or self reporting including data of machine operation and performance subsequent to machine repair, maintenance or upgrades and the data may collectively be used to enhance and adapt the rules used in analysis of machine data to predict future machine performance and future needs for machine repair, maintenance or upgrades.
  • the systems and methods can increase the availability of gaming machines, increases the mean time between machine failures and/or decreases the examples of machine down time.
  • systems and methods are provided by fully integrating each game device with sensors that monitor and record game device problems or events while accumulating and logging other diagnostic and prognostic data. Events can be prioritized, and business decisions can determine actions based on the priorities. In some examples, diagnostic and prognostic tests may be administered and events and trends accumulated and may be stored locally and later at a central controller.
  • systems and methods are provided for geographical mapping that may be displayed on a GUI that assists service technicians. For example, thousands of game devices distributed within thousands of retail locations over a wide geographical area which may be displayed in this manner.
  • systems and methods include states indicating when the device is up-and-running, or when the device is down and awaiting repair.
  • a third state can indicate that a device has a "minor" problem that is predicted to lead to a down state for a gaming device, for example, if left unaddressed.
  • the minor problem may be some intermittent operation or a parameter, for example a temperature increase that is not found in typical or otherwise properly operational game devices. The intermittent operation may be due to operator or game player abuse or neglect, unusual environments, like high humidity or dust, normal wear or other malfunction or breakage.
  • an "amber" state along with the "red” and "green” states may be provided.
  • the visualization can, in some examples, immediately informs the nearest technician, for example via geographical mapping so that the technician can be fully prepared for servicing one or several sites in the immediate area.
  • Disclosed methods and systems can, in some examples, allow the nearest technician to be alerted, the site and nearby sites and maintenance status of each may be displayed, accurate locations and traveling directions may be displayed, and the methods may include the step of assigning priorities by location, type of faults or problems and maintenance status (for example the risk of an actual device shutdown or complete malfunction), as well as financial aspects of the sites or machines.
  • systems and methods are provided that will identify and record examples or symptoms that indicate a potential fault or failure.
  • a predetermined time frame for servicing the device may be provided and a timely service visit may be scheduled.
  • a computer implemented method for generating a set of fault prediction rules may include the steps of operating one or a plurality of gaming devices and collecting event data from the gaming devices or from sensors associated with the gaming devices.
  • Event data from the gaming devices can, in some examples, be analyzed with a computer processor and a set of one or more fault prediction rules may be generated with a computer processor, based on the event data analysis and patterns of gaming device operation and faults.
  • the event data may include device malfunctions, device repair or service, or device operating parameters, such as temperature, performance test results, online accessibility; network availability (wireless or hard wired), sound operation, display operation, input device operation (for example buttons or other controllers, or other software or hardware operational parameters.
  • some examples method may also include steps to modify a set of one or more preexisting fault prediction rules based on the recently generated fault prediction rules or data analysis to form a new or updated fault prediction rule set.
  • the methods may include steps involving triggering diagnostic tests on a gaming device, for example, in response to the event data analysis. Additionally, or alternatively, the method may involve predicting a likelihood of a device malfunction and then providing an alert to a gaming server.
  • the method may include the step of analyzing device event data over a time window, which time window may be predetermined, against the fault prediction rule set and predicting the likelihood of device malfunction or performance issue.
  • the method may also include the step of receiving an indicator of a time window for the analysis, for example from a game operator or central controller.
  • the time window is separately determined, for example based on the analysis of aggregate or individual device event data.
  • the method may also include the steps of continuing to operate the one or more gaming devices during a period of continued operation and analyzing event data from the gaming devices during the period of continued operation.
  • the method may also involve generating an additional set of fault prediction rules event data analysis with a computer processor based on patterns of gaming device operation and faults during the period of continued operation or based on a combination of device operation and faults during the period of continued operation and a time period preceding the period of continued operation.
  • This method may then include the step of modifying the preexisting fault prediction rule set based on the additional set of prediction rules and forming a modified fault prediction rule set.
  • a method for predicting a performance issue with a gaming machine. This method may include the steps of monitoring a plurality of data elements representing operational characteristics of a gaming machine and then analyzing the plurality of data elements to determine whether the plurality of data elements are indicative of a risk of a performance issue with the gaming machine based on a set of rules for determining the risk of a performance issue. Subsequently a determination of whether or not the data elements are indicative of a risk of performance issue can be performed, and responsive to a determination that one or more of the data elements are indicative of a risk of a performance issue with the gaming machine, a signal can be transmitted to indicate the risk of a performance issue.
  • the risk of a performance issue may be analyzed for a particular time period.
  • the method may include receiving an indication that the gaming machine has been serviced and recording servicing data related to the servicing of the gaming machine. This servicing data may be included in the plurality of data elements analyzed to determine whether the plurality of data elements are indicative of a risk of a performance issue with the gaming machine based on a set of rules for determining the risk of a performance issue.
  • the method may include the steps of determining whether a gaming machine has been serviced, for example based on event data related to the gaming machine and upon determining that the machine has been serviced, suppressing a signal indicating the risk of a performance issue.
  • analyzing the plurality of data elements may include filtering the data elements.
  • the methods may include outputting a communication for receipt by a field technician, a game machine operator or a malfunction ticket handling system, where the communication indicates a risk of a performance issue.
  • the methods may include a server assembling and communicating data elements representing risks of performance issues.
  • a system may be provided to monitor and facilitate an analysis of data collected from a gaming machine that can be used to determine a level of risk of a performance issue with the machine.
  • the system may include a processor configured to collect data representing operational aspects of the gaming machine and transmit the collected data; a database configured to receive and store the collected data, where the database is operatively linked to the gaming machine; and a server operatively linked to the database, where the server can be configured to analyze the collected data and to generate a risk factor for a performance issue with the gaming machine, the risk factor being based on an analysis of the collected data and a set of rules for determining the risk of a performance issue with the gaming machine.
  • the server may also be configured to revise the set of rules for determining the risk of a performance issue with the gaming machine based on analysis of gaming machine performance data.
  • a non-transitory computer readable medium having instructions stored therein thereon When executed, the instructions are operable to monitor and facilitate an analysis of data collected from a gaming machine and to determine a level of risk of a performance issue with the machine and to generate and a set of rules for determining the risk of a performance issue with the gaming machine based on analysis of gaming machine performance data.
  • a method for generating fault prediction rules includes receiving, at a processor, event data of one or more gaming devices; and generating, at the processor, one or more fault prediction rules from the event data based on patterns of gaming device operation and faults.
  • a method of predicting a performance issue with a gaming machine includes receiving, at a processor, a plurality of data elements representing operational characteristics of the gaming machine; and upon determining that the plurality of data elements are indicative of a risk of the performance issue with the gaming machine based on a set of rules for determining the risk of the performance issue, generating an alert indicating the risk of the performance issue.
  • a system for determining a level of risk of a performance issue with a gaming machine includes a database configured to store data representing operational aspects of the gaming machine; and a server configured to generate a risk factor for a performance issue with the gaming machine, the risk factor being based on the stored data and a set of rules for determining the risk of a performance issue with the gaming machine.
  • Figure 1 is a schematic, block diagram of a network with client game devices, a retailer, and a central controller;
  • Figure 2 is a schematic, block diagram of a typical game device, retailer, and central controller
  • Figure 3 is a flow chart of operations according one embodiment of the invention.
  • Figure 4 is a timeline showing the relationship of certain events according to certain embodiments.
  • Figure 5 is another timeline showing the relationship of certain events according to certain embodiments.
  • Figure 6 depicts a block diagram showing certain components of an exemplary gaming machine.
  • Figure 7 depicts a flowchart illustrating an exemplary method of facilitating machine performance predictions.
  • Figure 8 depicts a flowchart illustrating another exemplary method of facilitating machine performance predictions.
  • Figure 9 depicts a flowchart illustrating another exemplary method of facilitating machine performance predictions.
  • Figure 1 is a schematic block diagram of a wide area computer network 2 that may include a plurality of game devices 4 communicating with a retailer 6 over a local area network 5 (LAN).
  • the retailer communicates via the Internet 8 or other WAN to a central controller 10.
  • a technician 12 may be contacted via the same communications network.
  • Game devices 4, retailers 6 and a central controller 10 may be in the form of computer servers or processors that may run one or more applications on one or more platforms (hardware and software) suitable for gaming applications.
  • game devices 4 can be VLTs (video lottery terminals) connected to retailers 6 (a retailer is a terminal that may act as a site or LAN controller) via LAN 5.
  • VLTs video lottery terminals
  • retailers 6 a retailer is a terminal that may act as a site or LAN controller
  • game devices 4 ca be networked with central controllers 10 directly or via a peer to peer network.
  • a central controller can be located at and/or operated by a retailer. In some examples, the central controller can be part of a game device. [0044] In some examples, the technician 12 can be located at the central controller 10. In some examples the central controller 10 and be operated by the technician 12.
  • Figure 2 is an illustrative block diagram of hardware and certain software modules that may be found in the game device 4, retailer 6, and/or central controller of Figure 2.
  • the central controller may have a more powerful implementation that performs many operations at high speeds, while the retailer may have more modest in hardware and processing speed, while the game device may have relatively modest hardware and software. Regardless, similar hardware may be found in each.
  • a computer bus 20 connects a processor 22 to memory 24; to I/O (Input/Output) interfaces 26; and to communications hardware 28 that connects to the network 30 (here the NETWORK 30 may be the Internet 8 or the LAN).
  • the processor 22 may be any processor or controller or control logic arranged to execute program code and exercise control over the retailer, central controller or game device. Processors made by Intel, AMD, or any other manufacturer may be used, as well as ASIC. (Application Specific Integrated Circuit) or other particular designs.
  • the memory 24 can includes a ROM and/or a RAM. Again, standard hardware may be used, including electronic or magnetic ROM and RAM, flash, optical, CD's, hard disk drives, etc. External memory, not shown, may be used and include disk and RAID systems.
  • the I/O interfaces 28 may include drives for motors. LED and other displays, printers, touch screens, mouse and keyboard or key, and other such interfaces.
  • the communications hardware 28 may include drivers for discrete wires, twisted pairs, Ethernets, Optical fiber, wireless and any other transmission types known to those skilled in the art.
  • a video display and/or a touch screen keyboard 25 can provide visual information for playing the game in a game device, or controlling and monitoring the devices on an LAN for the retailer.
  • a video display may operate as a graphical users interface (GUI).
  • GUI graphical users interface
  • the example memory is shown having: a data storage 130 for local operations, a communications program stack 132 that implements the communications hierarchy; an operating system 134, device drivers 136, memory and other system managers 138 and local game control and operation software 140. Other configurations can also be used.
  • Figure 3 is an example flow chart showing blocks associated with a method for generating fault prediction rules.
  • the method can be performed by a game device, a retailer, a central controller or a combination thereof.
  • a game device such as VLT 40, can detects and logs data associated with events that include issues or problems associated with the game device. These can include, for example, access doors, bill (currency) acceptors, printers, coin mechanisms, button or touch sensors, the device game itself, power supplies, software and hardware errors, and the like.
  • the game device can include one or more sensors for detecting issues or problems, and can, in some examples, generate event data.
  • components of the game device can generate signals, interrupts, or messages indicating issues or problems.
  • the data related to the events may include results from diagnostic and prognostic tests run on the game devices.
  • the data acquisition 42 can collects data on the video monitor 44, the printer 46 and the bill acceptor 48.
  • the data from these input/output devices may be logged, tracked, processed 50 and/or tagged with a priority.
  • a condition monitor 52 can apply prior established thresholds (developed heuristically or with historical data, for example) and can compare them to the data.
  • Self generated diagnostics 56 or prognostic 58 can be initiated by the retailer, the central controller, the VLT itself or a technician, and can test functions and operations of the VLT. In some examples results can be tracked and stored.
  • Prognostic tests 58 may be run to identify parts of the VLT that are at risk of future failure. Prognostics tests may be scheduled to run at recurring times, for example on a predetermined schedule or they may be performed on demand as required. In some examples, the data from these tests can generate decisions 60 on how to proceed. The responses 64 to these decisions may include communication among or between the retailer, the VLT system itself, the central controller, and/or a technician. In some examples, the graphical user interface (GUI) 62 can provide a display where the data, problem and/or decisions 60 for review by a technician or other personnel. In some examples, In some examples, the GUI can provide feed back or controls options for an operator to adjust reactions or responses of the system.
  • GUI graphical user interface
  • event data can be specific to a particular gaming site, such as a casino or a geographic location, or to a particular gaming machine, device, or model of gaming device.
  • the event data represents at least one malfunction selected from the group consisting of mechanical, electrical and software malfunctions or errors.
  • the event data may also represent machine status, certain problematic issues or machine errors.
  • data analysis can be used for prognosis determinations.
  • diagnostic and prognostic tests may be self administered locally within the LAN or by the VLTs themselves.
  • low priority events and trend data can be maintained in the LAN and sent to a central controller when some higher priority event is sent, or when the network traffic is low.
  • low priority and/or trend events can include, inter alia: higher than expected temperatures, printer paper getting low or jamming, a bill acceptor rejecting too many bills, button sticking, intermittent problems closing doors, or glitches on a display screen, etc.
  • Low priority events can include events that do not disable the operation of a game device, but can be repaired at a later time, for example, because they negatively affect the operation of the game device or the game playing experience.
  • high priority events suggest or call for more immediate attention and/or repair.
  • Such events can include any event that shuts down the game device, e.g., bill acceptor not working, a video display not working, the VLT not working, etc. These events can be reported via the retailer to the central controller immediately.
  • Some example methods disclosed herein can include escalating the reporting of a predicted fault to game operators, field technicians or other personnel.
  • ticketing systems can operate to see that the game machine is serviced or repaired as quickly as possible.
  • the central controller can coordinate and respond to reported problems in a comprehensive manner. For example, a high priority problem arises and is reported to the central controller, via the retailer, which may send the lower priority, trend and diagnostic data along with the high priority event.
  • the central controller may send low priority data regarding all the game devices at a site. If a service technician visit is necessary, the central controller may automatically inform the service technician about high and all the low priority issues at that site. The central controller may also automatically inform the service technician about low priority issues at other sites that will be on his geographic route to reach the site with the high priority problem.
  • the system can provide a technician with specific problems and/or symptoms related to all of the game devices at that site.
  • the technician can arrive with all the tools, including diagnostic software, and replacement parts specific to the problems to be addressed. In some examples, this can improve maintenance system efficiency.
  • a GUI may be available for a technician to enter test/diagnostic/prognostic programs.
  • a set of business rules 54 can be configured for facilitating responses to the data results for efficient maintenance of the gaming systems.
  • business rules may be applied that enhance the decision for types of responses to the problems. These rules may be amended to accommodate unforeseen problem that may arise.
  • low priority and informational data can be buffered (stored) at the local game device or the retailer and sent to the central controller during low network traffic times.
  • Informational data may include accounting information that is usually sent at the end of the business day. In some examples, this can preserve bandwidth or can avoid overloading systems at peak traffic times. In some examples, this can be applied to low bandwidth networks such as dial-up, cellular, satellite, leased lines or wireless, or to networks wherein costs vary based on the time of day.
  • a partial list of device events incorporated into an illustrative example of the present invention includes, for example: any access door open; the bill validator is: empty, miscounted, jammed, cheated, timed out, full, etc.; the printer is: offline, paper is low, no communications, paper jammed, cutter jammed, mechanism opened, misaligned, timed out (meaning some function had a time limit that expired), etc.; the coin mechanism is: mal-functioned, jammed, empty, electronic signal in error, etc.; touch sensors not responding, cash out buffer full; terminal disabled by retailer or central controller; event buffer full; batteries are low, games not installed or enabled or operating; a general system error; the system experienced a power down event; outdated versions of software or device drivers.
  • diagnostics are exercised other conditions that may be buffered include cooling fan speed, internal temperatures, presence and use of Flash memory, etc.
  • business rules 54 may include the following: a game device and the retailer buffer events including low and informational events and once a day (or weekly or biweekly or monthly or twice a day), the retailer will send the buffered data to the central controller at low network traffic time; whereas high priority events may be sent by the retailer immediately to the central controller; an authorized system person may be able to initiate and view the results of a self test or diagnostic for a game device from the central controller, the retailer or the game device itself; the central controller may be able to interrogate the retailer or game device for bill acceptor status, memory status, temperature, battery status; the data from self tests and diagnostics may be buffered for reporting and trending analyses. Trending data may include identifying and tracking usage of peripherals, electronic components, sensors, etc. In some examples, virtually all components found in a game device or retailer and the estimated or mean time between failures may be determined and displayed.
  • Priority can be shared by the game device, the retailer and the central controller. Priority can be high when, for example, business rules so determine. For example, high priority can be assigned when a game device is not operating or is malfunctioning in a way that prevents wagering activity or game playing or the machine is otherwise prevented from generating income, or the same or a similar situation occurs for an entire site of several game devices, or an entire LAN, an entire site or an entire gaming system. For example, in addition to the sensors on a game machine one or more sensors may be placed on the network to confirm that the game machines are operating, for example by a game machine returning an appropriate response following receipt of a status check message at the game machine.
  • the system can also be configured to allow for prioritization so that some sites, some LANs, or some game devices are assigned higher priorities. For example, those with greater commercial value may be assigned higher priority. Moreover, some geographic location may be more visible to the public than others or may be more important, for some reason, than others and so may be assigned higher priorities. The human owners of a gaming system or site may set their priorities as they see fit.
  • the business rules 54 may include buffered data mining where programs may process the buffered data to determine and identify key attributes of hardware and/or software failures that presage future failures. Predictive events may be visualized and maintenance steps taken or modified to minimize the effects of future failures. In this aspect, the business rules 54 may be amended and/or added to so as to efficiently implement maintenance procedures that minimize these future effects.
  • Accumulating data may include measuring and storing many different operating parameters and characteristics of game devices and LANs, etc. Trends in these parameters and characteristics may suggest preventative maintenance that pre-empts more costly future failure that might have occurred. Measuring and tracking power usage, down time, temperatures, time that a game device may be being played, or upcoming special usages or occasions may dictate that preventative maintenance.
  • One other important parameter may be the amount of time and type of servicing a technician may have spent on a game device, or at a site or on an LAN or WAN.
  • an Online Adaptive Diagnostics and Prognostics (OADP) system and methods are disclosed.
  • the OADP system and methods can be designed for gaming machines.
  • the individual gaming machines can send event codes and sensed data corresponding to status and/or problematic issues on the machines.
  • the event codes and sensed data may be stored in a database.
  • the disclosed system examines the event codes and sensed data received during a time-window (for example a predetermined or otherwise externally determined time frame) from a particular machine and diagnoses the machine.
  • the system also examines the received data to predict the machine status over a time horizon or at a defined point in the future.
  • the failure prediction systems and methods described herein can improve system uptime and availability by predicting the occurrence of certain types of hardware and software errors, thereby increasing system and gaming machine mean time between failures.
  • some machine faults can be predicted before they occur by comparing machine events and sensor data with a set of rules.
  • the set of rules may be dynamic and generated by an off-line process which examines the historical event codes and related data produced by different electronic gaming machines.
  • a feedback system or mechanism (which may consist entirely or partly of software) can be used to enhance and adapt the generated set of rules.
  • the set of rules may be dynamic and the system and methods increase the availability of gaming machines and increase the mean time between failures or other down time for the gaming machines. Overall repair and maintenance costs may also be reduced since certain costly repairs may be avoided.
  • Figure 4 is an example timeline 100 showing the relationship of certain events in accordance with example methods and systems of the application.
  • horizontal line 102 shows increasing time.
  • a method of failure prediction may be summarized as follows. Given the current state of an electronic gaming machine at time t 104, the method may predict the potential occurrence of a failure at a future time, tf 106, as shown in Figure 4.
  • the current state of a machine at present time 1 104 can be represented by the set of events that occurred within a data window (time-window) preceding t 104.
  • Potential occurrence of a failure within a prediction time window can be predicted based on the event patterns and data that occur within the time window and may also include event patterns and data that precede the time window.
  • Data mining techniques may be applied to identify and recognize patterns in the event data within the time window by analyzing a large set of event data.
  • the method can determine or identify the events or data that commonly leads to machine failures within the time-horizon using data mining techniques.
  • Figure 5 is an example timeline 120 depicting a series of events 122a, 122b and 122e that occur before a failure f 124, as shown in Figure 5.
  • horizontal line 102 shows increasing time.
  • Events 122a, 122b, and 122e occur in varying frequencies within a time window 124, leading up to a machine failure f 126 within a time horizon 128.
  • the systems and methods can predict the likelihood or risk of a failure within the time horizon 128.
  • the systems and methods can predict the likelihood or risk of a failure at a particular time point, such as time point 1 130.
  • An exemplary method to identify the failure patterns includes analyzing a large or otherwise statistically significant set of historical event data, referred to as training data.
  • the training data includes a set of event data from electronic gaming machines received by a device such as a central controller, though similar methods may rely on data from other machines.
  • the central controller can performs data analysis that results in the generation of a set of rules.
  • the rule generation can be time consuming due to the large size of the training data; hence, in some examples, it may run as an off-line process.
  • generating rules based on a training data set can include customization for different deployment cases.
  • the analysis can incorporate factors such as different types or models of gaming machines, the length of time that individual machines have been in the field, different models of peripherals in the gaming machines, different types of venues where the machines are installed and the maintenance or repair history of the different machines.
  • the size of the training data may be determined by three factors: (i) the event data time window 124; (ii) the time horizon 128 for prediction; and (iii) the employed protocol.
  • the time window 124 can identify and specify the length and identity of the time block the training is to consider and analyze when examining the historical data.
  • the time horizon 128 can specify the prediction horizon, that is the length and identity of the time block in the future that the system is to consider in determining predictions. In some examples, increasing the time horizon 128 may enhance the probability that a failure is predicted correctly, for example, when a failure is predicted.
  • the employed protocol can define the event codes to be analyzed.
  • the employed protocol can define the features used in training. Different protocols may have a different number and set of event codes. For example, one protocol may include as many as 400 different event codes, or even more.
  • the set of rules used for analysis can be generated off-line in a process that is separate from live or ongoing game play or machine operation.
  • the data set used to generate the rules may be fixed and unchanged throughout the data analysis steps.
  • the set of rules can be generated online in an adaptive technique, for example, during live game play or where the data set being analyzed is dynamic and updated, for example with regular or irregular updates, which may occur on a monthly, weekly, daily, hourly or even more frequent basis.
  • generating rules online or with an adaptive process can require less time and resource consuming data analysis. In the online or adaptive process the data can be automatically collected from gaming machine sites, for example, through the network.
  • a set of rules can be already in place and the systems and methods can operate to continue to monitor data from the gaming machines and update the existing rules.
  • the amount of data analyzed to update a set of rules may be less than amount of data necessary to create an initial set of rules from scratch.
  • Generating the training data may include the step of preprocessing the data, where software in the system may be configured to perform a preprocessing step, involving cleaning the collected data with a preprocessor.
  • Cleaning the data may involve any one or more of the following subtasks: noise reduction or removal, identification and removal of outlying data entries, and resolving inconsistencies in the data.
  • Cleaning may also refer to taking data in a raw or uncleaned state or form and converting the data into a form that is better suited for later data analysis steps, for example, data mining or modeling tasks. For example, cleaning may include processing or removal of extraneous or unnecessary data such as meta data, tags, or empty fields.
  • Software in the system may also be configured to filter the collected data.
  • filtering can include feature extraction where redundancies (i.e., attributes carrying duplicate or less information) can be eliminated by a function or ranking process.
  • Other techniques for data manipulation may also be used or they may be used in the alternative, for example wrapper, embedded and search based models of data management and manipulation.
  • the preprocessing step may be performed separately, in sequence or in parallel, or together with other steps.
  • the software module or engine(s) that perform these steps may be provided separately or together, for example filtering the data.
  • the time for the preprocessing the data may be reduced, since the total amount of data being processed may be reduced, when compared against the amount of data that would need to be preprocessed in the absence of a preexisting set of rules.
  • adaptive or online techniques may be configured to accommodate and responsively adjust to any mismatch arising when installing the prediction software at a new site. For example, when first installed at a new site, there may be a mismatch between the training data set which was generated using historical data from another site and the gaming machines and data being produced at the new site.
  • the adaptive techniques may be configured to avoid significant negative impact on the performance of the diagnostics and failure prediction or other issue prediction systems disclosed herein.
  • adaptive or online techniques can accommodate new features and data codes in the data set being generated by the gaming machines. Advances in electronic gaming machines, advances and developments in gaming machine diagnostics, as well as the use of new protocols, may all result in new data codes for new events being generated. This new data may necessitate generating new rules as well as new decision trees to handle and make machine operation performance predictions based on this new data.
  • the adaptive or online techniques can, in some examples, allow game operators to selectively include or exclude certain data codes for events from the decision process.
  • the adaptive techniques can operate in a flexible way to allow game operators this level of control and may even allow the system to generate new prediction rules or update existing rules based on a revised set of data codes or selected events.
  • the adaptive or online techniques can, in some examples, facilitate handling heterogeneous event codes (e.g., event codes from sites which have different models of machines or different machines manufactured by different vendors).
  • heterogeneous event codes e.g., event codes from sites which have different models of machines or different machines manufactured by different vendors.
  • the adaptive or online techniques may eliminate training steps. For example in the case of a new installation of the performance prediction software system, a set of preexisting rules may be used as a starting point, and the adaptive system can then customize and update that set of rules to reflect the unique experience and history of that gaming site as time passes and gaming machine performance data is generated and even as the gaming site evolves with new gaming machines being added and older gaming machines being removed, or even changes in the population of different features and options appearing in the suite of gaming machines at the site.
  • FIG. 6 depicts a block diagram showing certain components of an example gaming machine 200.
  • the gaming machine 200 may include a printer 210, a bill acceptor 212, a card reader 214, a display 216, a CPU temperature sensor 218 and a door sensor 220.
  • a gaming machine may be provided with various other sensors and components or it may also be provided with fewer sensors and components.
  • various other temperature sensors (not shown) may be provided, for example to monitor the temperature of other components of the gaming machine or the temperature of the gaming machine generally.
  • the door sensor 220 may be useful to indicate to a central controller whether the gaming machine door is closed or open, the latter position indicating a possible security breach.
  • Figure 7 depicts a flowchart illustrating an example method 300 of facilitating machine performance predictions in accordance with certain embodiments of the invention.
  • Figure 7 shows an example method of predicting potential failures in gaming machines.
  • the example method illustrates two processes: a rule generation process and failure prediction process.
  • the rules generation process can be used to characterize the failure and non-failure patterns of the gaming machines using data from the gaming machines which is collected in the form of event codes 310.
  • a software module or collection of software and hardware components, such as sensors, can send data to an event codes collector 312. Further details related to the source and transmission of event code data to the event codes collector 312 is provided below.
  • the data representing the event codes may be stored in a database, and in some embodiments the event codes 310 may be stored in the database.
  • a rules generator 314 can be in communication with the event codes database 310, as applicable, and may generate an initial set of rules using the existing event codes. Alternatively, the rules generator 314 may use a pre- generated set of rules as an initial online rules set 316. The rules generator 314 can adapt the online rules set 316, for example, in real time, depending on the observed event codes and the feedback information it receives through the event codes collector 312 related to the failure prediction.
  • the online rules set 316 can contain the set of rules to predict failures or other events such as performance issues. Initially this could be a set of predefined set of rules that has been generated off-line.
  • the set of rules can be dynamic and the number of elements in the set of rules R,
  • the rules generator 314 may generate the online rules 316. Alternatively, or additionally, the rules generator 314 may also update an existing set of online rules 316, for example based on ongoing analysis of the event codes database 3 0.
  • a clustering technique, trend analysis, decision tree, or any other suitable data analysis algorithm or collection thereof may be employed for this purpose.
  • the rule generator 314 may examine data leading up to a gaming machine failure to determine whether there might be identifiable trigger events, such as repeated occurrences of one or more event codes 310 or particular combinations of event codes 310 or examples of two or more event codes occurring within a particular time frame or examples of three or more event codes occurring within a particular time frame.
  • the event codes collector 312 can collect event data from the different gaming machines 318a, 318b, 318c and store them into the event codes database 310, for example upon receiving the events via the network 320.
  • Events data may be used for predicting a failure 320 or predicting a different performance related event and may include firmware fault, optical fault, component fault, and memory fault, among others.
  • step 322 results in a prediction of a fault (or other performance event) 326 then the method may proceed to perform diagnostics 328 on the gaming machine predicted to have a fault.
  • the fault prediction step 332 can operate as follows: for each gaming machine, the system can analyze the set of events that occurred during a defined time window and compare them against the online fault prediction rules 316. If a machine is expected to fail or experience a performance issue during the future time window, the system can generate an alert.
  • the alert can be a message sent to technician via e-mail, SMS, etc.
  • the system can trigger a situation manager 330 to display an alert.
  • the alert can trigger the issuance of a maintenance ticket in a help desk system.
  • the failure prediction step 322 may include considering repair data related to components that have been repaired or serviced such that the system will not generate false alarms and request additional unnecessary repairs for such machines.
  • This repair data may be maintained in a separate database (not shown) or it may be maintained within or as part of the event codes database 310. If a gaming machine has been serviced, repaired, or even replaced during the time window the failure prediction may be configured to suppress any alert generation for that particular machine.
  • the fault prediction alerts may be used to trigger diagnostic tests 328 to be performed by or on a gaming machine to verify failure predictions or performance predictions.
  • the fault prediction engine with the adaptive method may also be applied in a root cause analysis, for example, in an off-line mode, to analyze and predict faults in the repair shop of the operator.
  • the fault prediction analysis 322 may involve discovering which rule or rules match the current state of a gaming machine.
  • the current state of a gaming machine may be represented by a collection of events codes and corresponding attributes.
  • a similarity analysis may be used to find the best rule that matches the current state Sj.
  • the situation manager 330 may provide feedback to the rules generator 314 thereby allowing a game machine operator to specify false alarms and ignore or prevent certain predictions.
  • the rules generator 314 can include feedback information the next time it generates the online rules 316 and thereby can adapt the rules to reduce false alarms and increase prediction accuracy.
  • multiple rounds of event code 310 analysis and rule generating the system 300 can optimize the online rules 316 for predicting relevant faults or performance issues. In some examples, this may be achieved, through normalizing and developing a baseline for normal operation of a gaming machine and detecting any deviation towards a fault in the future based on any monitored event from the gaming machine.
  • Figure 8 depicts a flowchart illustrating an example method 400 for generating fault prediction rules.
  • event data can be received or collected from the gaming devices or from sensors associated with the gaming devices.
  • event data from the gaming devices can be analyzed 414 with a computer processor to generate 416 one or more fault prediction rules based on the event data and patterns of gaming device operation and faults.
  • the event data may include device malfunctions, device repair or service, or device operating parameters, such as temperature, performance test results, online accessibility; network availability (wireless or hard wired), sound operation, display operation, input device operation (for example buttons or other controllers), or other software or hardware operational parameters.
  • the fault prediction rules can be optionally updated 418 and in some examples, the method may then proceed back to the beginning 420, where the newly updated rules may be further updated based on additional data collected by the system.
  • Figure 9 depicts a flowchart illustrating an example method 500 for predicting a performance issue with a gaming machine.
  • This method may include receiving 510 data elements representing operational characteristics of a gaming machine.
  • the data elements can be analyzed 512 to determine 514 whether the plurality of data elements are indicative of a risk of a performance issue with the gaming machine 514. As described herein this determination can be based on a set of rules for determining the risk of a performance issue.
  • an alert can be generated 516 to indicate the risk of a performance issue.
  • the alert can be a signal or message sent to technician via e-mail, SMS, etc.
  • the W wireless mail
  • the 20 system can trigger a situation manager 330 to display an alert.
  • the alert can trigger the issuance of a maintenance ticket in a help desk system.
  • the risk of a performance issue may be analyzed for a particular time period.
  • the method may include receiving a data element indicating that the gaming machine has been serviced and recording servicing data related to the servicing of the gaming machine.
  • the method may also include filtering any predicted faults to remove any false positives, for example if a machine was newly deployed or had been recently serviced, or other known false positive result.
  • the methods may also include prioritizing the determined performance issues based on a severity of the performance issue or an assessed risk of game machine malfunction; as well as escalating the reporting of the determined performance issues to game operators, service technicians or malfunction ticketing systems.
  • the methods may also include the steps of providing a report of the determined performance issues via a graphical user interface, for example to a business intelligence unit for review by game operator executives, marketing personnel and other game operations persons or regulators.
  • the disclosed invention is applicable to both hardware and software failures and is applicable to gaming devices in online gaming, government sponsored and commercial gaming environments.
  • the methods described herein can be implemented with gaming devices 4 networked with a central controller 10, as illustrated for example in Fig. 1. In other examples, the methods can be implemented at a single site or retailer 6. The methods can also be implemented on a single gaming machine having a processor. In some examples, the methods can be implemented on multiple gaming machines networked together with one or more of the gaming machines having processors. In other examples, the methods can be implemented on any combination of these elements or other suitable combinations.
  • the above-described embodiments of the present invention can be implemented in any of numerous ways.
  • the embodiments may be implemented using hardware, software or a suitable combination thereof.
  • the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
  • processors may be implemented as integrated circuits, with one or more processors in an integrated circuit component.
  • a processor may be implemented using circuitry in any suitable format.
  • a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer.
  • the disclosed systems and methods are not restricted to electronic gaming machines, they would also apply to any functionally equivalent devices that can be used for gaming purposes such as personal computers, laptops, tablets, personal digital assistants (PDAs), mobile devices, a Web TV, a smart phone or any other suitable portable or fixed electronic device.
  • PDAs personal digital assistants
  • a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.
  • Such computers may be interconnected by one or more networks in any suitable form, including as a local area network or a wide area network, such as an enterprise network or the Internet.
  • networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.
  • the term "online” refers to such networked systems, including computers networked using, e.g., dedicated lines, telephone lines, cable or ISDN lines as well as wireless transmissions. Online systems include remote computers using, e.g., a local area network (LAN), a wide area network (WAN), the Internet, as well as various combinations of the foregoing.
  • LAN local area network
  • WAN wide area network
  • the Internet as well as various combinations of the foregoing.
  • Suitable user devices may connect to a network for example, any computing device that is capable of communicating over a network, such as a desktop, laptop or notebook computer, a mobile station or terminal, an entertainment appliance, a set-top box in communication with a display device, a wireless device such as a phone or smartphone, a game console, etc.
  • the term "online gaming” refers to those systems and methods that make use of such a network to allow a game player to make use of and engage in gaming activity through networked, or online systems, both remote and local.
  • “online gaming” includes gaming activity that is made available through a website on the Internet.
  • the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
  • the invention may be embodied as a tangible, non-transitory computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs (CD), optical discs, digital video disks (DVD), magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory, tangible computer-readable storage media) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above.
  • a tangible, non-transitory computer readable storage medium e.g., a computer memory, one or more floppy discs, compact discs (CD), optical discs, digital video disks (DVD), magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory, tangible computer-readable storage media
  • non-transitory computer-readable storage medium encompasses only a computer-readable medium that can be considered to be a manufacture (i.e., article of manufacture) or a machine and excludes transitory signals.
  • program or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the present invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
  • Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • functionality of the program modules may be combined or distributed as desired in various embodiments.
  • data structures may be stored in computer-readable media in any suitable form.
  • data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields.
  • any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags, addresses or other mechanisms that establish relationship between data elements.
  • the invention may be embodied as a method, of which several examples haves been provided.
  • the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

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Abstract

L'invention concerne des systèmes et procédés pour un système de maintenance pronostique et diagnostique prédictif en ligne. Les systèmes et procédés peuvent être configurés pour une utilisation avec des machines de jeu mises en réseau. Les systèmes et procédés peuvent fonctionner en temps réel et détecter et analyser des données représentant divers indicateurs de performance de machine ou une diminution actuelle ou future des performances de la machine. Les données peuvent représenter ou être utilisées pour la prévision des performances de la machine et des risques de défaillance et l'identification des réparations, maintenances recommandées ou nécessaires ou d'autres problèmes de performances. Dans un autre mode de réalisation, des systèmes et procédés sont prévus pour une analyse automatisée des données relatives au fonctionnement de la machine et à la génération de règles liées à la prévision des besoins de performance, de réparation et de maintenance futurs des machines.
PCT/CA2013/000499 2013-01-31 2013-05-23 Prévision de défaillances et de problèmes de performances WO2014117245A1 (fr)

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US10921792B2 (en) 2017-12-21 2021-02-16 Machinesense Llc Edge cloud-based resin material drying system and method
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CN111008732A (zh) * 2019-11-21 2020-04-14 中南大学 一种基于堆栈模型的故障预测方法及系统
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