METHOD FOR COMPUTER-ASSISTED PREDICTION OF FUTURE OPERATING CONDITIONS OF MACHINE COMPONENTS

Abstract

The invention relates to a method for the computer-aided forecasting of future operating states of machine components that makes it possible to predict reproducibly and with a high degree of certainty a forthcoming event in the life cycle of a machine component. For this purpose, a parameter that is representative of the state of a machine component under consideration is selected for the component and is acquired as a measured value in the course of the use of the machine component. While taking into consideration the application concerned for which the machine component is being used, the acquired measured value is fed into an automatically running process to find a recommendation for action. Properties and the influencing variables determining these properties, the applications coming into consideration for them, the influencing variables to which the machine component is exposed in the respective application and the resultant influencing variables have in this case been formulated and determined for the machine component under consideration, and these influencing variables should be considered in the interpretation of the acquired measured value and the recommendation for action deduced from it. Findings obtained during the use of the machine component with respect to the influencing variables or findings pertaining to them that have been obtained by systematic experimental investigations are used to ascertain the interactions and effects of the influencing variables and bring them into a relationship with the damage events determined. The items of information ascertained in relation to the interactions and the damage events influenced by them are fed into a database. The application, the acquired measured value and the assigned influencing variables are fed as input variables into a selection algorithm running on a computer which, taking into consideration the input variables and the acquired measured value, selects the recommendation for action that is optimally appropriate for the use in the respective application on the basis of the information stored in the database.

Description

Methods for computer-aided prediction of future operating states of Machine components The invention relates to a method with which future states, such as an impending damage event, a maintenance requirement, reaching the maximum service life and the like, can be predicted with computer assistance. The term "machine component" is used below to refer to individual machine elements or groups of interacting machine elements combined into specific assemblies. The machine elements under consideration each represent the smallest technical functional unit capable of fulfilling the technical and functional requirements arising from the respective technical application. To meet requirements. Accordingly, the term "machine component" also includes the individual components used in the manufacture of machine components or their machine elements. In many applications, machine components of the type discussed here are subjected to high loads of various kinds during practical use. These loads can result from forces absorbed by the respective component or from environmental conditions (e.g., ambient temperature, atmosphere) under which the machine component operates. Among the Machine components exposed to such loads include, in particular, moving parts such as wheels, rollers, cylinders, shafts, rings, chains, belts, etc. Timing belts, V-belts, flat belts, or multi-V belts, used, for example, in drives; pulleys or pulley profiles; seals; gears; springs; hoses; or assemblies that are themselves moved under load or whose components move under load during operation, such as gears, transmissions, drive units in general, especially belt drives like toothed belt drives, motors, clutches in general, especially slip clutches, clamping sets / shaft-hub connections, freewheels, rolling bearings, plain bearings, brakes, drive trains, and the like. Each of these machine components is generally essential to the function of the machine in which it is used. Consequently, a malfunction regularly occurs if the respective machine component does not function properly or even fails.The economic damage caused by such a disruption can be considerable, especially in large-scale industrial manufacturing. Therefore, machine components are typically subjected to regular maintenance and are replaced or repaired in a timely manner to minimize the risk of spontaneous failure. This maintenance work generally follows a schedule provided by the manufacturer of the machine or machine component. Maintenance plan taking into account empirical data, design criteria and the operating conditions. Furthermore, for particularly critical machine components, it is common practice to systematically monitor certain parameters, such as temperature development, absorbed loads, and the like, in order to prevent overloads of the components in question. to prevent component wear and premature failure. If the monitored parameter reaches a specific limit, this is typically interpreted as a warning signal, triggering an action. This action might involve, for example, reducing the load that caused the limit to be exceeded, in order to bring the monitored parameter back into its intended normal range, or performing maintenance on the relevant machine component to prevent wear-related failure and thus ensure its continued functionality. to ensure the component. The threshold values, the reaching of which triggers the respective action, are also generally based on empirical values, which are either from the The machine manufacturer may communicate this to the machine operator, or the machine operator may determine it based on practical experience in use. The problem is that both the acquisition of the parameter(s) essential for the operating state of the respective machine component under consideration and the determination of the respective limit value always require an interpretation of measured values ​​and operating conditions. The result of this interpretation depends either on the experience of the machine operator or the machine operator, i.e., the person who operates and monitors the machine, or on the quality of the empirical data and the interpretation model provided by the manufacturer of the respective component. machine component or the machine equipped with it The method is based on recommendations for action that it provides in the event that the monitored parameter reaches the threshold value deemed critical. Against the background of the aforementioned procedure, which is common in operational practice, the task arose to create a method with which an impending event in the life cycle of a machine component can be predicted with a high degree of certainty and reproducibility. The invention has solved this problem by the method specified in claim 1. Advantageous embodiments of the invention are specified in the dependent claims and are explained in detail below. The inventive method for computer-aided prediction of future operating states of machine components therefore comprises the following steps: A) For each machine component under consideration, at least one parameter is determined selected which is representative of the condition of the machine component. B) The parameter is recorded as a measured value during the ongoing operation of the machine component. C) The recorded measurement value is fed into an automated process to determine a recommended course of action, taking into account the application in which the machine component is used, whereby the following The following procedure is used as a basis: Ca) For each machine component under consideration, properties and the influencing factors determining these properties have been identified. Cb) For each machine component under consideration, the possible applications have been determined, the influencing factors to which the machine component is exposed in the respective application have been identified, the influencing factors that arise have been formulated, and it has been determined which influencing factors are to be considered accordingly when interpreting the recorded measured value and the resulting recommendation for action. Cc) Out in practical use of the machine component under consideration Based on statements obtained about the influencing factors or statements related to the influencing factors obtained through systematic experimental investigations, the interactions and effects of the influencing factors are determined and related to specific Damage events have occurred. If, during the ongoing use of the procedure, it turns out that such previously unidentified influencing factors and effects exist, these newly identified influencing factors or effects will be added to the group of those to be considered. added to the considerations and interactions with other influencing factors or related damage events. If the product- or application-specific interactions can be described using a formula, this rule is formulated as soon as the interactions become known. Alternatively or additionally, the interactions can also be captured in the form of fuzzy descriptions ("fuzzy logic," artificial neural networks, other artificial intelligence methods) of the relationships. Cd) The interactions and those thereby influenced in step Cc) Information gathered from damage events (influencing factors, rules, vague descriptions of interactions) is fed into a database as machine-readable data. This database can comprise one, two, or more individual databases, each tailored to specific use cases and thus linked to specific functions. The database contains machine-readable data related to damage events. Information gathered regarding mutually influencing requirements and factors is optionally linked in the database in such a way that changes to one piece of information have a direct impact on the other piece(s) linked to it. The database thus forms a network of interrelated and specific data. Information was generated from linked machine components, relating to a variety of different applications and associated damage events. Ce) The respective application, the recorded measurement value and the associated Influencing factors are fed as input variables into a selection algorithm running on a computer, which, taking into account the input variables and the recorded measurement value, selects the most suitable recommendation for action for use in the respective application based on the information stored in the database. The recorded measurement value is interpreted based on the information stored in the database, which is linked together and based on practical experience, thus providing a recommendation for action that takes into account the specific characteristics of the respective application. In this process, the following are required for the derivation of a method that is specific to the application and takes into account the measured value. Recommendations for action have been formulated regarding the influencing factors that arise in the application in question (work step Cb)). To continuously improve the database created by work steps Ca) - Cd) for the selection step (work step Ce)), the following additional work steps can be completed: Cf) The machine component for which the respective measured value is recorded and interpreted in the manner specified in step Ce) is uniquely identified with respect to the application in which it is used. Cg) For this purpose, the selected machine component is identified and monitored in its practical use using the identification means. The identification means can not only carry information that uniquely identifies the respective machine component, but also contain information about the nature of the machine component, in order to easily assign the measured values ​​obtained through monitoring to a specific combination of properties of the machine component under consideration. In its simplest form, monitoring provides information not only on the recorded measurement but also on at least one other characteristic value, such as the time elapsed between the start of use and the recording of the respective measurement. Additionally, monitoring can provide information on two or more further characteristic values, such as the external influences (temperature, atmosphere) to which the machine component was exposed during operation. The identification device itself can also be equipped to provide information about the internal condition and the loads on the machine element during operation. Of course, other known measurement methods can also be used to record the forces acting on the machine element during use, its changes in length, temperature development, and the like (see...).For example, for a belt drive, see US 6,264,577 B1 , DE 10 2010 002 551 AI , JP 2009-007078 A, DE 44 44 263 C1 , US 5,843,258 A, AU 2009203049 AI ). Ch) The statements recorded in step Cg) are fed into the process beginning with step Cc) as influencing factors. With the optionally additional work steps Cf) to Ch), a A self-learning selection system has been created that continuously improves and increasingly reflects practical reality. Of course, not every machine component selected according to the invention needs to be... It is not necessary to go through additional work steps Cf) to Ch), but it may be sufficient if at least one machine component, ideally a representative number of machine components, is monitored in the respective application. The invention is therefore based on the same principles as the method for selecting a machine component presented in German patent application 10 2015 107 176.2, the contents of which are incorporated into the present application by reference. The invention extends the aforementioned method described in German patent application 10 2015 107 176.2 by adding the possibility of determining a recommendation for action based on a measured value recorded for a specific machine component, taking into account the influencing factors that are typical for the respective application and systematically linked. The method according to the invention thus enables, starting from the respective measured value and on the basis of the influencing factors and their relationships determined for the respective application and identified through systematic test series or the recording of practical experience, a reliable, automatically generated prediction of the position at which the respective machine component under consideration is located in its life cycle. The resulting recommendation for action accurately reflects the actual need for action. Thus, the recommendation for action determined according to the invention can be applied within a specific time period. Maintenance to be carried out with the aim of maintaining the normal condition of the machine component may consist of a recommendation for replacement by a specific, named time in order to prevent a spontaneous failure that is highly likely to occur after this time, or of waiting if the recorded measurement has proven to be non-critical, i.e., confirming a normal condition. Since the action recommendation determined according to the invention is given at a time when no damaging event has yet occurred, the invention allows This approach offers a high degree of planning reliability. Maintenance work or the replacement of a machine component can then be carried out at a time when the machine is scheduled to be down, such as on a weekend or during routine maintenance scheduled for other reasons. Maintenance work. To assign influencing factors or measured values ​​to specific uses of machine components, a so-called "application key" can be defined. This key is applied in a standardized form and links the assigned influencing factors together. Such an application key enables the machine to process the task of identifying the most suitable application for each application. The recommended course of action can be simplified. The essential aspect of the inventive method is that, in an automated process based on empirically determined or theoretically formulated information stored in a database, the machine element optimally suited for the respective application is accurately identified, starting from the specific application itself. The information used for this purpose, stored in the database, describes the relationships between the respective application, the influencing factors applicable to it, and their interactions. In the sense of a self-learning system, the quality and reliability of this information can be continuously improved by repeatedly feeding in newly acquired data. The system according to the invention can be designed such that, in new situations, it For applications for which there is no directly assigned information in the database, the next most suitable application can be determined based on the influencing factors assigned to this new application and the interactions determined or formulated for each of them from the information stored in the database. Based on this next most suitable application, a suggestion for a suitable machine element can then be made. In this way, an optimally suited machine element can be quickly determined for a new application in an iterative process, without the need for complex and time-consuming trials. The advantages of the method according to the invention therefore consist in - a detailed, application-specific analysis and dimensioning of the design according to any criteria, - optimal utilization of the product or material properties actually inherent in the respective machine element under consideration and the resulting under Taking into account the specific characteristics of the respective application and the resulting actual life expectancy, - the targeted avoidance of replacing the part too early or too late machine element and the associated optimal utilization of the resource that the respective machine element represents, - the avoidance of uncontrolled failures, which in the prior art can be caused by the selection of machine elements that are sufficient according to the selection criteria considered so far, but prove to be unsuitable in practice, or by unrecognized interactions of influencing factors, - the continuous improvement of application-specific predictions Questions, and - the targeted, economical product development for specific problems, whereby the particular advantage of the inventive procedure lies in the fact that not only individual components or groups can be considered as machine components in the selection, but that the result of the inventive procedure in determining the component optimized for the respective application can also be suggestions for the selection of specific components, materials or process parameters with which a machine element or an assembly formed from several interacting machine elements ("aggregate") can be produced, each of which is optimally adapted to the requirements arising from the intended use. Application-related influencing factors can be - mechanical loads (speed, torque, preload forces, drive geometry, friction pairing, load changes, load cycles, speeds, accelerations, tensile or compressive stresses, other dynamic or static loads); - Environmental conditions (atmosphere in which the application takes place, ambient humidity and temperature, pollution, material pairing, pressure), - Properties of the material from which the machine component is made (Material behavior when heated, under load, under load changes, under friction) - geometric shape and quality of the machine component. The measurement result recorded at the respective monitored machine component can be transmitted to a central computer system using any conceivable data transmission device. This system stores the information used according to the invention and performs the respective interpretation of the measured value. The computer system then returns the action recommendation derived from the measured value to the machine operator, also via a suitable data transmission device, or automatically initiates any necessary steps that may result from it. A typical embodiment of the invention is described below. Fig. 2 shows a conveyor device F in a top view. In modern automobile manufacturing, the individual components, the body, or the partially assembled vehicle are transported on sleds along conveyor tracks. The sleds S of such conveyor systems F, also known in practice as "SKID systems," run on rollers 1, which are mounted on shafts 2, 3. One shaft 2 is driven by an electric motor 4, while the other shafts 3 are coupled to the driven first shaft 2 via a belt drive 5. The rollers 1 have a coating 6 on their circumference made of a polyurethane material ("PU material") or another elastomer. The coating 6 has Accordingly, a certain elasticity on the one hand and a high friction on the other hand are required to ensure a safe and sufficiently damped coupling of the sleds to be driven (http: / / www.logsystems.de / skid.html). The rollers of SKID systems are subject to high loads and a This results in correspondingly high wear. At the same time, the unforeseen failure of such a roller 1 can mean the shutdown of an entire production line. To avoid this, rollers 1 in conventional SKID systems are replaced at regular, fixed intervals. In a skid system F according to the invention, at least one of the rollers 1 is equipped with a sensor 7 which detects the temperature development in the PU coating 6 of the roller 1. In the course of the inventive method, the known applications in which, for example, the rollers 1 considered here as examples are used are recorded. For this purpose, customer data, applications and products used, production conditions, operating conditions, etc., are recorded in detail in an application key. The typical application scenario for the respective application is then simulated on suitable test benches, and, for this specific application, tests such as lifetime tests are carried out. The insights gained in this way from practical customer experience or from the test results become part of the pool of interconnected information stored in the database. This information represents the expected service life under the respective operating conditions, as well as the expected performance in the "SKID system" application. Determine the operational behavior of roles 1 and correlate this, for example, with the Temperature development in the PU material of the PU coating 6 of roll 1. Sensor 7 sends the measured values ​​it has recorded to a central data processing system (not shown here), where the measured values ​​are evaluated and a recommendation for action is derived from this evaluation, based on which a replacement or general maintenance of roller 1 may subsequently be carried out. Accordingly, the temperature of the PU coating 6 measured by sensor 7 is normally within a specific normal temperature range. With increasing operating time, the rolling behavior of the carriages S on the rollers 1 changes as a result of various influencing factors, such as aging of the coating, material variations or fatigue, geometric errors in the alignment of the individual components relative to each other, etc. Alignment errors, changes in the state of the sleds S in contact the surrounding area of ​​the rollers 1 as a result of damage or abrasion. Wear and tear increase, causing the temperature in the casing 6 to rise. Consequently, the temperature measured by sensor 7 also increases. The temperature detected by sensor 7 is used as an input variable in the system on the The data processing facility, running the interpretation system shown in Fig. 1, is fed in and uses the stored findings on the influencing factors that are essential in the application case "SKID system" and the correlated with them. The temperature development of the casing 6 of the roller 1 was compared. This results in... For example, if the system interprets the temperature rise as progressing at a rate deviating from a standard pattern, indicating complete failure (i.e., reaching the service life limit) within a specific, near period, it recommends replacing roller 1 at a safe distance before reaching that period. However, if the recorded temperature shows that the temperature trend is due to, for example, seasonal warming of the If this is due to ambient temperature or the like, then the The recommendation to proceed as normal was issued, indicating no need for action. The temperature profile recorded by sensor 7 and any other available information about the operating conditions under which the rollers 1 are used are compared with the information stored in the interpretation system. The actual wear condition of each item can be determined by random sampling. The replaced roll 1 must be checked. If a significant deviation between the predicted state and the actual state is found, the recorded temperature profile and the associated additional information about the operating conditions are incorporated into the The database underlying the interpretation system is updated, and the information stored there regarding influencing factors and rules for coupling and mutually influencing these factors is updated accordingly. Similarly, information about unforeseen damage events that were not predicted or were incorrectly predicted by the interpretation system is fed into the system and used to refine the rules and influencing factors stored there. In this way, the interpretation system is continuously improved, so that the accuracy of the forecast results leading to the respective recommendations for action is also continuously improved. REFERENCE MARK F Funding facilities S sled 1 roll 2,3 waves 4 electric motor 5 Belt drive 6. Casing of the rollers 1 7 Sensor

Claims

PATENT CLAIMS 1. Method for computer-aided prediction of future operating states of machine components, comprising the following work steps: A) For each machine component under consideration, at least one A parameter was selected that is representative of the state of the machine component. B) The parameter is used during the ongoing operation of the machine component as Measurement data recorded. C) Taking into account the application case in which the machine component is used, the recorded measurement value is fed into an automated process to determine a recommended course of action, based on the following procedure: Ca) For each machine component under consideration, properties and the influencing factors determining these properties have been identified. Cb) For each machine component under consideration, the following are in question The upcoming applications were determined, the influencing factors to which the machine component is exposed in the respective application were identified, the influencing factors that arise were formulated, and it was determined which influencing factors should be considered accordingly when interpreting the recorded measurement value and the resulting recommendation for action. Cc) Based on statements about the influencing factors obtained during the practical use of the machine component under consideration, or based on statements relating to the influencing factors obtained through systematic experimental investigations, the interactions and effects of the influencing factors are determined and related to specific damage events. Cd) The interactions described in step Cc) and thereby Information obtained from affected damage events is fed into a database as machine-readable data. Ce) The respective application, the recorded measurement value and the associated Influencing factors are fed as input variables into a selection algorithm running on a computer, which, taking into account the input variables and the recorded measurement value, uses the information stored in the database to determine the appropriate parameters for use in the respective application. The application selects the most suitable course of action. The method according to claim 1, characterized in that, in the event that, during the ongoing use of the method in step Cc), it becomes apparent that unidentified influencing factors exist, these newly identified factors will be identified. Influencing factors for the group of influencing factors to be considered and which interact with other influencing factors or are related to them Damage events added. Method according to one of the preceding claims, thereby characterized in that the database into which the previously determined information is fed in step Cd) comprises one, two or more individual databases, each containing machine-readable data related to specific use cases. Method according to one of the preceding claims, thereby characterized by the fact that the ongoing improvement of the In addition to the work steps Ca) - Cd) for the selection step (work step Ce)), the following work steps must be completed on the database created: Cf) The machine component for which the respective measured value is recorded and interpreted in the manner specified in step Ce) is uniquely identified with respect to the application in which it is used. Cg) For this purpose, the selected machine component is identified and monitored in its practical use using the identification means. Ch) The statements recorded in step Cg) are fed into the process beginning with step Cc) as influencing factors.

5. The method according to claim 4, characterized in that the Identification means in addition to information that uniquely identifies the respective machine component, information about the nature of the Contains machine component.

6. Method according to claim 4 or 5, characterized in that in step Cg) the time period is recorded which elapses between the start of use and a replacement necessitated by wear.

7. Method according to one of claims 4 to 6, characterized in that the monitoring in step Cg) provides information about a set of two or more characteristic values.

8. Method according to one of claims 4 to 7, characterized in that the identification means is equipped in such a way as to provide information about the internal state and the loads of the machine element during operation.