Procedure for the maintenance of a plurality of similar components for aircraft and system

Abstract

Method (1) for maintaining a plurality of similar components (2) for aircraft (3), wherein each of the similar components (2) is identifiable via a unique identification code (4), wherein the method (1) comprises the following method steps: - creating an error database (10); - creating a calculation model (11) based on an error data set (10) and a historical subset (13) of an operating data set (6); - determining the probability (14) for the presence and / or future occurrence of at least one of the predefined state categories (8) individualized for individual components (2) of the similar components (2) on the basis of a current subset (12) of the operating data set (6), by processing the current subset (12) by the created calculation model (11); - generating an action instruction (15) based on the determined probability (14) for the presence and / or future occurrence of one of the predefined state categories (8); and f) Carrying out a maintenance measure on one of the similar components (2) on the basis of the generated instruction (15).

Description

[0001] The present invention relates to a method for maintaining a plurality of similar components for aircraft having the features of the preamble of claim 1 and to a system having the features of the preamble of claim 15.

[0002] Various approaches to maintaining aircraft components are known from the state of the art. A maintenance event can, for example, simply include the inspection of a component, the replacement of specific operating resources or subcomponents, the repair of the component, or the replacement of the entire component. A fundamental distinction is made between planned and unplanned maintenance measures. Planned maintenance events can, for example, be carried out at predetermined intervals, such as monthly or annually, or after a predefined number of flight hours or flight cycles have been reached. The intervals of such planned maintenance events are usually chosen so that any technical faults that occur do not disrupt operations. These must be distinguished from unplanned maintenance events, which must be carried out due to an unexpected technical fault.Since unplanned maintenance events disrupt flight operations, airlines and maintenance organizations aim to reduce the proportion of unplanned maintenance events.

[0003] For this purpose, state-of-the-art approaches for component condition monitoring exist. By evaluating specific measured values, attempts are made to determine the technical condition of the component in real time (diagnosis) or to predict its remaining operating time (forecast). These approaches can be used to initiate appropriate maintenance measures before a technical failure occurs, or to optimally utilize the intervals between maintenance measures. Condition monitoring can thus make flight operations safer and more cost-efficient.

[0004] US Pat. No. 10,228,687 B2 discloses a method for diagnosing a fault in an air conditioning pack. For this purpose, data from such an air conditioning pack is compared with threshold values.

[0005] The object of the present application is to provide an improved method for maintaining a plurality of similar components for aircraft and a correspondingly improved system.

[0006] The object is achieved by the features of the independent claims. Further preferred embodiments of the invention can be found in the dependent claims, the figures, and the associated description.

[0007] According to a first aspect of this application, the object is achieved by a method for maintaining a plurality of similar components for aircraft, wherein each of the similar components is identifiable via a unique identification code, the method comprising the following method steps: Step a) Carrying out maintenance measures on at least a subset of the similar components, wherein carrying out a maintenance measure comprises in each case determining possible error states and, if an error state exists, assigning the determined error state to a state category from a plurality of predefined state categories. Step b) Saving the condition categories thus determined, each linked to the identification code of the component in which the fault condition was detected, and linked to a timestamp indicating the time at which the fault condition was detected, as a fault data set. Step c) Creating a calculation model that is designed to determine the probability of the presence and / or future occurrence of at least one of the predefined condition categories individually for individual components of the similar components on the basis of a current subset of an operating data set, wherein the operating data set comprises measured values ​​with which the operating condition can be individually characterized for individual components of the similar components, wherein within the operating data set the measured values ​​are each linked to the identification code of the corresponding component and to a time stamp assigned to the respective measured value, wherein the creation of the calculation model is carried out on the basis of a historical subset of the operating data set and on the basis of the error data set, wherein within the historical subset the measured values ​​are linked to time stamps,which precede the timestamps associated with the measured values ​​of the current subset. Preferably, the timestamps are assigned to the measured values ​​when the measured values ​​are first stored on a data storage device. Step d) Determining the probability of the presence and / or future occurrence of at least one of the predefined condition categories individualized for individual components of the similar components on the basis of a current subset of the operational data set by processing the current subset through the created calculation model. Step e) Generating an instruction to carry out a maintenance measure individualized for individual components of the similar components based on the determined probability of the presence and / or future occurrence of one of the predefined condition categories. Step f) Carrying out a maintenance measure on one of the similar components based on the generated instruction.

[0008] The invention recognizes that the information contained in the error database contributes significantly to creating a high-quality calculation model. By considering this information together with the information from the operational database, the calculation model can calculate the probability of the existence and / or future occurrence of a specific condition category with sufficiently high accuracy. The calculation model can thus be used to diagnose and / or predict the existence or occurrence of condition categories, thereby making component maintenance more efficient. Based on this condition category, for example, the error state underlying the condition category can be deduced, or the possible error states can at least be narrowed down.

[0009] By using the historical subset of the operating data and the error data to create the calculation model, it is possible to determine relationships between the determined condition categories and the recorded measured values. This is particularly facilitated by the use of condition categories, which serve as a kind of "label" to categorize the various error states in a practical way in maintenance operations. The use of predefined condition categories facilitates their automated processing, allowing them to be used efficiently to create the calculation model.

[0010] By examining a plurality of components of the same type and documenting the detected fault conditions by assigning them to predefined condition categories, causal relationships between the change in one or more measured values ​​and the presence of a fault condition underlying a condition category can be identified and taken into account when developing the action instruction. Furthermore, the similar components are also operated in a variety of aircraft, preferably in aircraft of the same type, so that a sufficiently large amount of data can be provided for the creation of the calculation model. Thus, the proposed calculation model is capable of calculating the probability of the existence and / or future occurrence of a specific condition category with improved accuracy.By carrying out the process steps a) to f), the overall system reliability of the similar components is improved.

[0011] An instruction within the meaning of this application can comprise all activities that are typically involved in the maintenance of the corresponding component; this can include, for example, the replacement of the entire component or the replacement of subcomponents; this can also include, for example, the repair of subcomponents and / or the performance of adjustment and / or setting activities. The instruction can, for example, be issued in text and / or speech form. The instruction can, for example, refer to measures described in a manual from the manufacturer of the aircraft or component. If the aircraft is an airplane, reference can be made, for example, to an Aircraft Maintenance Manual (AMM) and / or a Component Maintenance Manual (CMM).

[0012] By issuing the action instruction individually for a specific component, time-consuming troubleshooting can be avoided. The action instruction for a component with a specific identification code is preferably generated based on a current subset of the operational dataset that was also generated by this component. It goes without saying that the current subset of the operational dataset can be continuously updated and / or expanded during operation.

[0013] The operational dataset preferably comprises a plurality of operational datasets. Such an operational dataset comprises at least one measured value, an identification code, and a timestamp. Naturally, such an operational dataset may also include additional data fields, such as the registration number of the aircraft in which the component was operating when the measured values ​​were generated. Depending on the timestamp of the operational datasets, these are assigned to the historical subset or the current subset of the operational dataset. The operational dataset can be continuously expanded with new operational datasets, which are then made available to the calculation model as input data. Over time, operational datasets from the current subset of the operational dataset can become part of the historical subset of the operational dataset, allowing the calculation model to be improved based on these.

[0014] Accordingly, the error dataset preferably also comprises a plurality of error data records. Such an error dataset includes at least one status category, an identification code, and a timestamp. Of course, such an error dataset can also include additional data fields. The error dataset can, for example, also be updated and / or expanded over time with additional error data records.

[0015] The operational data records and / or the error data records can, for example, be distributed across different physical storage devices. The operational data records can, for example, be distributed across storage devices of different aircraft and / or across storage devices of stationary computers at one or more ground stations.

[0016] For the purposes of this application, similar components are understood to mean components of identical construction or essentially identical components. Identical components are interchangeable for installation in aircraft. Essentially identical components include, in particular, components that are similar in terms of their technical function and technical performance data, but have different interfaces, for example, different connections, for installation in the aircraft.

[0017] It goes without saying that even after the operational data set has been divided into a historical and a current subset, the link between measured values, time stamps and identification codes remains intact.

[0018] The characterization of the operating state within the meaning of this application can be done using a wide variety of operating parameters. An operating parameter can, for example, be a state (e.g., "on" or "off," "open" or "closed"), but also a physical quantity, such as pressure, temperature, or mass flow.

[0019] Preferably, the aircraft are of the same type, for example Airbus A320 aircraft, i.e. aircraft of essentially the same design.

[0020] Preferably, the creation of the operational dataset comprises the following process steps: Step A) Operating a plurality of aircraft, each of which incorporates at least one of the identical components. Step B) Generation of the measured values ​​with which the operating status of individual components of the similar components can be characterized when they are in operation in the respective aircraft. Step C) Saving the generated measured values ​​linked to the identification codes of the corresponding components and with the corresponding time stamps as an operating data set.

[0021] Through steps A) to C), the operational data set can be generated in such a way that the data is suitable both for creating the calculation model and as input data for the calculation model.

[0022] It is further proposed that operational data generated by one of the aircraft during operation be transmitted to a ground station, with the calculation model being executed on a computer at the ground station. The operational data is preferably transmitted during the flight, for example in the form of an Aircraft Condition Monitoring System (ACMS) report, and / or the transmission takes place after landing, for example in the form of full flight data. Of course, alternative transmission paths are also possible, for example, using an internet connection. The ground station can also comprise various spatially separated subunits.Preferably, the ground station comprises one or more data storage devices with a database on which the current subset of the operational data set, the historical subset of the operational data set and the error data set are stored.

[0023] It is further proposed that at least two of the same components are installed in one of the aircraft, wherein a measured value characterizing the operating state of the first component in the aircraft is compared with a measured value characterizing the operating state of the second component within the same aircraft, wherein the comparison of these measured values ​​is an additional component of the historical subset of the operating dataset and / or the current subset of the operating dataset, and / or the comparison of these measured values ​​is carried out by the calculation model. The two components of an aircraft are preferably operated in parallel over time. Preferably, the comparison comprises forming the difference between the two measured values ​​that have the same or approximately the same time stamp, taking into account the sign of the determined difference value.The two measured values ​​being compared preferably come from corresponding sensors of the two similar components installed within the same aircraft. By comparing two measured values ​​from two similar components installed in the same aircraft, the database for determining condition categories is improved. For example, the simultaneous increase in a certain measured value for both components of an aircraft may indicate that this increase is not due to a fault condition, but rather, for example, to a change in the environmental conditions or the flight status. This can improve the overall quality of the calculation model's results.In order to be able to assign the two components intended for use in one and the same aircraft to the corresponding aircraft, the operational dataset preferably includes another type of identification code that identifies the aircraft in which the component is installed during data generation. This additional type of identification code is then also linked to the associated measured values.

[0024] Preferably, a state category is additionally provided that characterizes an intact system state of one of the similar components. In method step a), if a fault condition is not present, the system is assigned to this state category. Thus, the absence of a fault condition can also be used as information for creating the calculation model.

[0025] Preferably, step d) is repeated with newly generated data that is added to the current subset of the operational dataset or that replaces the current subset, so that the probability of the presence and / or future occurrence of at least one of the predefined condition categories is provided in an updated form at predetermined intervals. Thus, the probability of the presence and / or future occurrence of at least one of the predefined condition categories can be continuously determined. If necessary, a countermeasure is created in the form of an instruction for a maintenance measure.By continuously determining these probabilities, the maintenance measure can be carried out in a particularly advantageous manner before the fault condition underlying the condition category occurs; the maintenance measure can thus be carried out as a preventive maintenance measure.

[0026] It is further proposed that step a) comprise the following sub-steps: carrying out maintenance measures on at least a subset of the similar components in an installed state, wherein in the installed state the respective component is installed in one of the aircraft; and carrying out maintenance measures on at least a subset of the similar components in a removed state, wherein in the removed state the respective component is removed from one of the aircraft. It has been shown that by taking into account all of the available maintenance data relating to the similar components for the creation of the calculation model, significantly better results can be achieved. The maintenance of components can generally only be carried out to a limited extent when installed on the aircraft.Maintenance measures requiring a higher level of detail and / or greater disassembly than in the installed state can only be performed in a suitably equipped workshop. In the installed state, different fault conditions are often diagnosed than in the removed state. Thus, the database can be improved by using data generated during maintenance measures in the installed and removed states. For the purposes of this application, a removed state is defined as a state in which the component is neither structurally nor functionally connected to an aircraft. In other words, the component can be transported independently of the aircraft in the removed state.

[0027] It is further proposed that each of the similar components comprises at least one sensor with which measured values ​​are generated during operation to characterize the operating state of this component; and / or that each of the aircraft comprises at least one sensor with which measured values ​​are generated during operation to characterize the operating state of one of the similar components installed in the aircraft. In this way, the measured values ​​required for the calculation model can be generated particularly efficiently. It is also not absolutely necessary for the sensors to be part of the component itself; therefore, for example, sensors provided in the aircraft for monitoring adjacent components are also possible. Different sensor types are possible, for example sensors for determining a temperature, the position of a control element, an air pressure and / or a flow rate.

[0028] Preferably, the calculation model is configured to determine a general system state individually for individual components of the similar components. For example, an action instruction can only be issued when a critical general system state of the component is reached. The general system state can be determined, for example, based on the determined probability for the presence and / or future occurrence of at least one of the predefined state categories.

[0029] Preferably, the general system state is represented by a numerical value, wherein the calculation model is configured to compare the numerical value representing the general system state with a first and a second threshold value, wherein a signal is output if the numerical value is between the first threshold value and the second threshold value, and wherein the generation of the action instruction according to step e) is triggered if the numerical value exceeds the second threshold value. The numerical value for representing the general system state offers the advantage that it can be processed simply and efficiently. The threshold values ​​are determined empirically, for example.

[0030] Preferably, the calculation model in step c) is created and / or adapted at least partially through machine learning, with the historical subset of the operational dataset and the error dataset being used as training data for machine learning. It has been shown that machine learning can be used to particularly efficiently determine causal relationships between the error dataset and the historical subset of the operational dataset. In this way, the quality of the calculation model can be increased.

[0031] It has proven advantageous to implement machine learning using so-called boosting algorithms. The integration of boosting algorithms can improve the performance of diagnosing and / or predicting the presence or occurrence of condition categories. These algorithms have been shown to improve prediction accuracy and overall performance in various machine learning tasks. Boosting algorithms are particularly suitable for the intended application due to their adaptability and versatility. Particularly noteworthy in the context of this intended application is their ability to skillfully manage complex data relationships and mitigate noise.

[0032] It is further proposed that each of the similar components be an air conditioning system comprising a cooling turbine, at least one, preferably four, heat exchangers, and a flow control valve. An air conditioning system, also referred to as an air conditioning pack (ACP), can lead to operational restrictions, unpleasantly high cabin temperatures, or even smoke development in the event of a fault. For this reason, the proposed method has particularly advantageous effects when applied to such an air conditioning system. The component referred to here as the cooling turbine is technically known as an air cycle machine (ACM); in addition to a turbine, it also comprises a compressor.

[0033] Preferably, the plurality of predefined condition categories includes one or more of the following condition categories: faulty cooling turbine, faulty heat exchanger, and / or faulty flow control valve. By considering at least one of these condition categories, a significant improvement in the system reliability of air conditioning systems can be achieved.

[0034] According to a second aspect of this application, the object mentioned at the outset is achieved by a system with a fleet comprising a plurality of aircraft and a plurality of similar components, in particular a plurality of similar air conditioning devices comprising a cooling turbine, at least one, preferably four, heat exchangers and at least one flow control valve, wherein at least one of the similar components is installed in each of the aircraft, wherein the maintenance of at least a subset of the similar components is carried out using the method according to the first aspect of this application, optionally taking into account the further developments described above, wherein the system comprises a computer program product which is configured to carry out the method steps c) to e) according to the first aspect of this application.With regard to the technical effects and advantages associated with the proposed system according to the second aspect of this application, reference is made to the preceding explanations in connection with the method according to the first aspect of this application. Preferably, the computer program product is loaded into an internal memory of a computer, which is, for example, part of the ground station, and is executed by this computer.

[0035] The invention is explained below using preferred embodiments with reference to the accompanying figures. In the figures: Fig. 1 a schematic representation of a component; Fig. 2 a first schematic representation of a method, Fig. 3 a second schematic representation of a method; Fig. 4 a schematic representation of a comparison of measured values; Fig. 5 a temporal progression of a general health status of a component; Fig. 6 temporal progression of the probability of the presence of different state categories; and Fig. 7 a system.

[0036] Fig. 1 shows a component 2 for an aircraft 3 (see Figure 7), namely an air conditioning device 23 for an aircraft, also referred to as an Air Conditioning Pack (ACP).

[0037] The air conditioning system 23 includes a flow control valve 26, four heat exchangers 25a to 25d, and a cooling turbine 24, also referred to as an air cycle machine (ACM). The cooling turbine 24, in turn, includes a compressor 32 and a turbine 33.

[0038] By means of the air conditioning system 23, bleed air 30 from an engine (not shown) can be cooled in order to flow into the cabin of an aircraft 3 at the appropriate temperature. The flow control valve 26 adjusts the amount of bleed air 30 supplied from the corresponding engine to the air conditioning system 23. Furthermore, the air conditioning system 23 comprises a flow channel 31 through which cold ram air 34 flows.

[0039] The bleed air 30 is taken from a hot high-pressure region of the engine and flows through the flow control valve 26 and then via a first heat exchanger 25a, which is cooled by the relatively cold ram air 34 of the flow channel 31. From the first heat exchanger 25a, a portion of the cooled bleed air 30 flows into the compressor 32 of the cooling turbine 24, so that the temperature is increased again by the compression in the compressor 32. The temperature difference between the bleed air 30 and the ram air 34 at the heat exchanger 25b is thus increased in order to release more heat energy there to the ram air 34 flowing past. From the heat exchanger 25b, the bleed air 30 then flows via the two further heat exchangers 25c and 25d, releasing heat energy, into the turbine 33 of the cooling turbine 24; The heat exchanger 25c is flowed through twice on the way to the turbine 33.Downstream of the heat exchanger 25d, a water separator 42 is provided for separating condensed water. The expansion in the turbine 33 causes the bleed air 30 to cool further significantly. After exiting the turbine 33, the bleed air 30 flows through the heat exchanger 25d once more to absorb thermal energy. The bleed air 30 then flows at approximately room temperature via an interface 36 into a flow line 35 of the aircraft 3. Furthermore, a bypass valve 37 is shown, via which the portion of the bleed air 30 that bypasses the compressor 32 can be adjusted; this portion of the bleed air 30 then flows via a bypass 38 directly to the outlet side of the turbine 32. The amount of ram air 34 flowing into the flow channel 31 is defined by the position of a flap 39, the position of which can be adjusted via an actuator 40. Furthermore, . Fig. 1 schematically shows on the right side a further component 2 in the form of an air conditioning device 23, which is connected to another flow line 35 of the same aircraft 3. The structure and function of the second air conditioning device 23, shown only schematically on the right side, correspond to the structure and function of the left air conditioning device 23 shown in detail.

[0040] Furthermore, the Fig. 1 various sensors 18a-18h, with which measured values ​​5 (see for example Fig. 2 and Fig. 4) are generated to characterize the operating state of the air conditioning device 23. By means of a sensor 18a, the valve position of the flow control valve 26 is determined as measured value 5. By means of a further sensor 18b, the differential pressure of the bleed air 30 in a flow channel upstream of the flow control valve 26 with respect to the environment 41 is determined as measured value 5. By means of a sensor 18c, the pressure within the flow line upstream of the flow control valve 26 is determined as measured value 5, i.e. the pressure present at the inlet side of the air conditioning device 23. Furthermore, by means of a sensor 18d, the valve position of the bypass valve 37 is determined as measured value 5. Furthermore, by means of the sensor 18e, the outlet temperature of the bleed air 30 from the compressor 32 is determined as measured value 5.Furthermore, a sensor 18f is provided, with which the position of the flap 39 for controlling the amount of incoming ram air 34 is determined as measured value 5. By means of the sensor 18g, the temperature downstream of the water separator 42 is determined as measured value 5. Finally, the outlet temperature of the air conditioning device 23 is determined as measured value 5 by the sensor 18h, i.e., the temperature at which the cooled bleed air 30 is delivered to the flow line 35.

[0041] Air conditioning devices 23 as described above are already known from the prior art.

[0042] The measured values ​​5 determined by the sensors 18a to 18h are used as input data for the method 1 described below (see Fig. 2, Fig. 3 and Fig. 7) is used to maintain a plurality of the air conditioning systems 23. The following description of this method 1 using the air conditioning system 23 of an aircraft is to be understood only as an example; it can also be applied to other similar components 2 of an aircraft.

[0043] Fig. Figure 2 schematically shows a method 1 for maintaining a plurality of air conditioning devices 23, the structure and functioning of which are already known from the Fig. 1 is known. The measured values ​​5a and 5b are shown as examples, which characterize the operating state individually for one of the air conditioning devices 23. The measured values ​​5a and 5b are, for example, measured values ​​5, which were measured with two of the sensors 8a to 18h from Fig. 1. For the sake of clarity, only two measured values ​​5a and 5b are shown graphically here. The air conditioning device 23 in question is assigned an identification code 4a. It is indicated graphically that such measured values ​​5a and 5b are also recorded for other air conditioning devices 23 with the identification codes 4b and 4c. Of course, in practice the measured values ​​5a and 5b will be recorded for more than just three similar components 2 in the form of air conditioning devices 23. The measured values ​​5a and 5b of the air conditioning devices 23, together with the identification codes 4a, 4b and 4c assigned to them and with corresponding time stamps 9, which are assigned when the measured values ​​5a and 5b are saved, form an operating data set 6, which is divided into a historical subset 13 and a current subset 12.The current subset 12 of the operational data set 6 has younger timestamps 9 than the historical subset 13 of the operational data set 6. In other words: the measured values ​​5a, 5b of the historical subset 13 of the operational data set 6 are older than the current subset 12 of the operational data set 6.

[0044] Furthermore, Fig. 2 schematically shows the creation of an error database 10. For this purpose, a process step a) is first carried out, the embedding of which in the process steps A)-C) and b)-f) is subsequently explained using the Fig. 3 is explained in detail.

[0045] Method step a) comprises performing maintenance measures on a plurality of air conditioning devices 23, wherein performing these maintenance measures each comprises determining a') possible error states 7 and, if an error state 7 is present, assigning a'') the determined error state 7 to a state category 8 from a plurality of predefined state categories 8. In this exemplary embodiment, a catalog comprising a plurality of error states 8 for various error states 7 is provided. For example, when performing the maintenance measure, it can be determined that no error is present; the absence of an error is then also assigned to a specially provided state category 8b, which indicates an intact system state 46.The condition categories 8a and 8b are linked to corresponding time stamps 9 and identification codes 4a to 4c; they thus form the error data set 10.

[0046] Furthermore, Fig. 2, the historical subset 13 of the operating data set 6, together with the error data set 10, forms training data 43, which is used to create—and in this embodiment also to optimize—the calculation model 11. The training data 43 generated in this way allows for the determination of relationships between changes in the measured values ​​5a, 5b and specific operating states. The arrow 47 indicates the time of detection of a specific error state 7a during a maintenance measure in step a), which is assigned to the state category 8a. The measured values ​​5a and 5b are plotted here as a function of time. It is evident that the function values ​​exhibit significant fluctuations before the detection of the error state 7a.By combining the corresponding measured values ​​5a and 5b with the determined condition category 8a, the calculation model 11 can be created in such a way that it can assign certain characteristics of the temporal progression of the measured values ​​5a and 5b to a specific condition category 8. This applies in particular the larger the number of similar components 2 whose data is available for creating the operating data set 6 and the error data set 10. Furthermore, an arrow 48 indicates the time of detection of an intact system state 46. It is evident that the measured values ​​5a and 5b do not exhibit any anomalies before the time indicated by the arrow 48. When creating the calculation model 11, this information is used to define the actual existence of condition category 8b intact system state 46.

[0047] The calculation model 11 is created here using machine learning (ML) methods using training data 43. In this exemplary embodiment, the machine learning methods also include the use of so-called boosting algorithms. However, alternative algorithms can also be used.

[0048] The calculation model 11 created on the basis of the historical subset 13 of the operational data set 6 and on the basis of the error data set 10 is set up to continuously process a current subset 13 of the operational data set 6. For example, the data of the current subset 13 can be made available to the calculation model 11 as input data. The calculation model 11 processes this input data in such a way that the probability 14 (see Fig. 6) for the presence - and in this embodiment also for the future occurrence - of at least one of the predefined state categories 8a, 8b can be determined individually for individual components 2 of the similar components 2.

[0049] Based on the determined probability 14, an instruction 15 for performing a maintenance measure is generated by means of the calculation model 11, customized for the affected air conditioning system 23. Based on this instruction 15, the instruction 15 proposed by the calculation model 11 is then implemented on the affected air conditioning system 23 in a method step f). Furthermore, the calculation model 11 can also be configured to output the diagnosed or predicted condition category 8 on which the instruction 15 is based. The maintenance personnel thus know the category of the fault that is to be remedied by the instruction 15. The calculation model 11 can be subjected to a validation 44.

[0050] Fig. 3 shows schematically the process of the proposed method 1: By means of the process steps A) to C), the operating data set 6 is generated.

[0051] In step A), a plurality of aircraft 3 (see Fig. 7), in each of which two of the air conditioning systems 23 are installed. In a step B), measured values ​​5 are generated, with which the operating state of individual air conditioning systems 23 of the plurality of air conditioning systems 23 can be characterized when they are in operation in the respective aircraft 3. The generated measured values ​​5 are then stored in a step C) linked to the identification codes 4 of the corresponding air conditioning systems 23 and with the time stamps 9 as operating data set 6. Of course, the operating data set 6 can be continuously supplemented.

[0052] In step a), a maintenance measure is carried out on at least a subset of the air conditioning devices 23, wherein the implementation of a maintenance measure includes the determination of possible error states 7. If an error state 7 is determined, it is assigned to a corresponding state category 8 from a plurality of predefined state categories 8. If the air conditioning device 23 has an intact system state 46 (see Fig. 2), the corresponding condition category 8b (see Fig. 2) awarded.

[0053] In a step b), the condition categories 8 thus determined over a longer period of time for a plurality of air conditioning devices 23 are linked to the identification code 4 of the air conditioning device 23 in which the corresponding fault condition 7 was detected, and linked to a time stamp 9 which identifies the time at which the fault condition 7 was detected, and stored as fault data set 10.

[0054] In a step c), the calculation model 11 is created. It is designed to be individualized for individual air conditioning devices 23 of the similar air conditioning devices 23 on the basis of the current subset 12 (see Fig. 2) of the operating data set 6 the probability 14 (see Fig. 6) for the presence and / or future occurrence of at least one of the predefined condition categories 8 and to output them. As already explained above, the operating data set 6 comprises the measured values ​​5 with which the operating state can be individually characterized for individual air conditioning systems 23 of the similar air conditioning systems 23. In the operating data set 6, the measured values ​​5 are each linked to the identification code 4 of the corresponding air conditioning system 23 and to a time stamp 9 assigned to the measured values ​​5 during storage. The calculation model 11 is then created based on the historical subset 13 of the operating data set 12 and on the basis of the error data set 10, i.e., based on the training data 43.

[0055] In a step d), the probability 14 for the presence and / or future occurrence of at least one of the predefined condition categories 8 is determined individually for individual air conditioning devices 23 of the similar air conditioning devices 23 on the basis of a current subset 12 of the operating data set 6 by processing the current subset 12 by the created calculation model 11.

[0056] In a step e), the action instruction 15 for carrying out a corresponding maintenance measure is generated individually for individual air conditioning systems 23 of the similar air conditioning systems 23 on the basis of the determined probability 14 for the presence and / or future occurrence of one of the predefined condition categories 8.

[0057] Finally, in a step f), a maintenance measure is carried out on one of the similar air conditioning systems 23 on the basis of the generated action instruction 15.

[0058] Fig. Figure 4 schematically shows the comparison of corresponding measured values ​​5a to 5e from two different air conditioning systems 23 that were installed in the same aircraft 23 at the same time. The measured values ​​5a to 5e were recorded for the first and second air conditioning systems 23, respectively; since they are provided with time stamps 9, the measured values ​​5a to 5e can be plotted as a function of time. Fig. 4, the measured values ​​5a to 5e for the first and second air conditioning systems 23 are shown as separate curves in the corresponding diagram. By calculating the difference between the identical measured values ​​5 of the two air conditioning systems 23 at defined times, a comparison of the measured values ​​5a to 5e of the two air conditioning systems 23 can then be made. The signs are taken into account when calculating the difference.

[0059] Fig. 5 shows a temporal progression of a general system state 20 of an air conditioning system 23. The general system state 20 is a numerical value determined from the probabilities 14 for the presence of certain state categories 8, as determined by the calculation model 11. The general system state 20 is also determined using the calculation model 11. With a value of 0, the general system state 20 is very good; with a value of 1.0, the general system state 20 is very poor. Furthermore, two threshold values ​​21 and 22, which were determined empirically, are shown in the diagram. If the general system state 20 exceeds the first threshold value 21 but still falls below the second threshold value 22, the calculation model 11 outputs a signal in the form of a warning. The maintenance personnel are thus informed of a possible future fault state 7.If the general system state 20 exceeds the second threshold value 22, the calculation model 11 triggers the generation of an action instruction 15.

[0060] Fig. 6 shows, by way of example, the temporal progression of the probabilities 14 for the presence of the following condition categories 8: defective cooling turbine 24, defective flow control valve 26 and defective heat exchanger 25. These failure probabilities are determined by the calculation model 11.

[0061] Finally, the Fig. 7 shows a system 100 with which the method 1 described above can be applied. It comprises a fleet 53 with a plurality of aircraft 3 in the form of airplanes, each of which comprises two of the similar components 2 in the form of the air conditioning systems 23.

[0062] It is schematically shown that the aircraft 3 generate data during operation that is part of the operational dataset 6. Within an operational dataset, one or more measured values ​​5 are linked to a corresponding identification code 4 and a corresponding timestamp 9. A plurality of such operational datasets form the operational dataset 6. It goes without saying that the operational dataset 6 does not have to be stored at a single physical location.

[0063] Furthermore, maintenance 51 can be seen schematically, which is divided into aircraft maintenance 52 and component maintenance 49. In aircraft maintenance 52, the maintenance measures can be performed on the air conditioning system 23 in an installed state 16; a condition category 8 determined in this way, together with the identification code 4 and a time stamp 9, then becomes part of the error dataset 10 as a fault data record. In component maintenance 49, the maintenance measure is performed on the air conditioning system 23 in a disassembled state 17; the spatial separation of the air conditioning system 23 from the aircraft 3 is also shown graphically. The condition category 8 determined in the disassembled state 17, together with the identification code 4 and a time stamp 9, then becomes part of the error dataset 10 as a fault data record.

[0064] The historical subset 13 of the operational data set 6 and the error data set 10 are used, as described above, to create and optimize the calculation model 11. The calculation model 11 is loaded as a computer program product 27 into the internal memory of a computer 29. The computer 29 is part of a ground station 28.

[0065] Furthermore, the Fig. 7 shows, by way of example, an aircraft 3 of fleet 53 that is in flight operation. The aircraft generates measured values ​​5 with which the operating state can be individually characterized for both installed air conditioning systems 23. This is done using the sensors 18 of the air conditioning systems 23 themselves, but here, for example, also using sensors 19 that are assigned to the aircraft 3 but are not part of the air conditioning systems 23. The corresponding measured values ​​5 are each linked to an identification code 4 and a timestamp 9 and transmitted wirelessly as an operating data record during the flight to a ground station 28. Of course, the transmission of the operating data records on the ground is also possible. The operating data records generated in this way are initially part of the current subset 12 of the operating dataset 6. They are made available to the calculation model 11 as input data.Any resulting instruction 15 and / or the general system status (see . Fig. 5) and / or the probabilities 14 (see Fig. 6) are displayed to the maintenance personnel on a display device 50, for example, a display. When an action instruction 15 is displayed, the maintenance personnel carry out the action on the corresponding air conditioning system 23. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 10,228,687 B2

[0004]

Claims

[1] Method (1) for maintaining a plurality of similar components (2) for aircraft (3), each of the similar components (2) being identifiable by a unique identification code (4), the method (1) being characterized by the following method steps: a) carrying out maintenance measures on at least a subset of the similar components (2), wherein carrying out a maintenance measure comprises in each case determining possible error states (7) and, if an error state (7) is present, assigning the determined error state (7) to a state category (8) from a plurality of predefined state categories (8); b) storing the state categories (8) thus determined, each linked to the identification code (4) of the component (2) in which the fault state (7) was detected, and linked to a time stamp (9) which identifies the time at which the fault state (7) was detected, as a fault data set (10); c) Creating a calculation model (11) that is configured to determine, individually for individual components (2) of the similar components (2), the probability (14) for the presence and / or future occurrence of at least one of the predefined state categories (8) on the basis of a current subset (12) of an operating data set (6), wherein the operating data set (6) comprises measured values ​​(5) with which the operating state can be characterized individually for individual components (2) of the similar components (2), wherein within the operating data set (6), the measured values ​​(5) are each linked to the identification code (4) of the corresponding component (2) and to a time stamp (9) assigned to the respective measured value (5), wherein the creation of the calculation model (11) is carried out on the basis of a historical subset (13) of the operating data set (6) and on the basis of the error data set (10),wherein within the historical subset (13) the measured values ​​(5) are linked to time stamps (9) which are temporally earlier than the time stamps (9) with which the measured values ​​(5) of the current subset (12) are linked;, d) determining the probability (14) for the presence and / or future occurrence of at least one of the predefined state categories (8) individualized for individual components (2) of the similar components (2) on the basis of a current subset (12) of the operating data set (6), by processing the current subset (12) by the created calculation model (11); e) generating an instruction (15) for carrying out a maintenance measure individualised for individual components (2) of the similar components (2) on the basis of the determined probability (14) for the presence and / or future occurrence of one of the predefined condition categories (8); and f) Carrying out a maintenance measure on one of the similar components (2) on the basis of the generated instruction (15). [2] Method according to claim 1, characterized by that the creation of the operating data set (6) comprises the following process steps: A) operating a plurality of aircraft (3), each of which has at least one of the similar components (2) installed; and B) generating the measured values ​​(5) with which the operating state of individual components (2) of the similar components (2) can be characterized when they are in operation in the respective aircraft (3); C) Saving the generated measured values ​​(5) linked to the identification codes (4) of the corresponding components (2) and with the corresponding time stamps (9) as an operating data set (6). [3] Method according to claim 2, characterized by , that - data of the operational database (6) generated by one of the aircraft (3) during operation are transmitted to a ground station (28), and wherein - the calculation model (11) is executed on a computer (29) of the ground station (28). [4] Method (1) according to one of claims 2 or 3, characterized by , that - at least two of the identical components (2) are installed in one of the aircraft (3), wherein - a measured value (5) characterising the operating state of the first component in the aircraft (3) is compared with a measured value (5) characterising the operating state of the second component (2) within the same aircraft (3), - wherein the comparison of these measured values ​​(5) is an additional component of the historical subset (13) of the operational data set (6) and / or the current subset (12) of the operational data set (6), and / or the comparison of these measured values ​​(5) is carried out by the calculation model (11). [5] Method (1) according to one of the preceding claims, characterized by , that - an additional state category (8) is provided which characterizes an intact system state (46) of one of the similar components (2), wherein - in process step a), if an error state (7) is not present, the assignment to this state category (8) takes place. [6] Method (1) according to one of the preceding claims, characterized by , that - step d) is repeated with newly generated data which are added to the current subset (12) of the operational data set (6) or which replace the current subset, so that the probability (14) for the presence and / or future occurrence of at least one of the predefined state categories (8) is provided in an updated manner at predetermined time intervals. [7] Method (1) according to one of the preceding claims, characterized by that step a) comprises the following sub-steps: - carrying out maintenance measures on at least a subset of the similar components (2) in an installed state (16), wherein in the installed state (16) the respective component (2) is installed in one of the aircraft (3); and - Carrying out maintenance measures on at least a subset of the similar components (2) in a disassembly state (17), wherein in the disassembly state (17) the respective component (2) is removed from one of the aircraft (3). [8] Method (1) according to one of the preceding claims, characterized by , that - each of the similar components (2) comprises at least one sensor (18) with which measured values ​​(5) are generated during operation to characterise the operating state of this component (2); and / or - each of the aircraft (3) comprises at least one sensor (19) with which, during operation, measured values ​​(5) are generated to characterise the operating state of one of the components (2) of the same type installed in the aircraft (3). [9] Method (1) according to one of the preceding claims, characterized by , that - the calculation model (11) is designed to determine a general system state (20) individually for individual components (2) of the similar components (2). [10] Method (1) according to claim 9, characterized by , that - the general system state (20) is determined on the basis of the determined probability (14) for the presence and / or future occurrence of at least one of the predefined state categories (8). [11] Method (1) according to one of claims 9 or 10, characterized by , that - the general system state (20) is represented by a numerical value, where - the calculation model (11) is designed to compare the numerical value representing the general system state (20) with a first and a second threshold value (21, 22), wherein - a signal is output at a numerical value between the first threshold value (21) and the second threshold value (22), and wherein - if a numerical value exceeds the second threshold value (22), the generation of the action instruction (15) according to step e) is triggered. [12] Method (1) according to one of the preceding claims, characterized by , that - the calculation model (11) in step c) is created and / or adapted at least partially by machine learning (ML), wherein - the historical subset (13) of the operational data set (6) and the error data set (10) are used as training data (43) for machine learning (ML). [13] Method (1) according to one of the preceding claims, characterized by , that - the similar components (2) are each an air conditioning device (23) comprising a cooling turbine (24), at least one, preferably four, heat exchangers (25) and a flow control valve (26). [14] Method (1) according to claim 13, characterized by , that - the plurality of predefined condition categories (8) comprises one or more of the following condition categories (8): defective cooling turbine (24), defective heat exchanger (25) and / or defective flow control valve (26). [15] System (100) with a fleet comprising a plurality of aircraft (3) and a plurality of similar components (2), in particular a plurality of similar air conditioning devices (23) comprising a cooling turbine (24), at least one, preferably four, heat exchangers (25) and at least one flow control valve (26), wherein at least one of the similar components (2) is installed in each of the aircraft (3), characterized by , that - the maintenance of at least a subset of the similar components (2) is carried out using the method (1) according to one of the preceding claims, wherein - the system (100) comprises a computer program product (27) which is configured to carry out the method steps c) to e) according to claim 1.

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