Evaluation of installation quality of elevator
By training a machine learning model with simulation data, the quality of elevators was evaluated, which solved the problems caused by technician errors during elevator installation and enabled more accurate and detailed quality assessment and improvement guidance.
Patent Information
- Application Number
- CN202380097844.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-05-12
- Publication Date
- 2025-12-12
AI Technical Summary
There are quality problems caused by technician errors during the existing elevator installation process, and traditional testing operations are simple but easily affected.
The system employs a machine learning model trained on simulation data. By receiving elevator operation data, the machine learning model evaluates elevator quality and generates quality reports or signals indicating unacceptable conditions, providing improvement measures.
It improves the accuracy and complexity of elevator installation quality assessment, reduces the impact of technician errors, and provides detailed quality assessment and improvement guidance.
Smart Images
Figure CN121127431A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present invention relates generally to the technical field of elevators. More specifically, the present invention relates to the evaluation of installations of elevators. BACKGROUND
[0002] Elevator installations are operations comprising various tasks in order to maintain the safety of the elevator operation in all situations. Specifically, an installation can refer to the case where an elevator is built into e.g. a new building, or it can refer to the case where one or more entities of an elevator are replaced in the context of a maintenance operation on the elevator system in question. The traditional way of arranging an installation in the context described is to complete the installation work and a technician performs tests and / or checks according to a test plan. These measures are performed before the installation is accepted for normal operation and handed over from the installation party to the party managing it, e.g. a building manager. Even though this method is so operational, it is susceptible to errors performed by the technician, e.g. during the tests. Furthermore, the known test operations are equally very simple.
[0003] Therefore, there is a need to introduce a more complex method to improve the quality of elevator installations. SUMMARY
[0004] The following presents a simplified summary in order to provide a basic understanding of some aspects of various application embodiments. The summary is not an extensive overview of the application. It is neither intended to identify key or critical elements of the application nor to delineate the scope of the application. The
[0005] It is an object of the present invention to provide a method, apparatus and computer program for generating data describing the quality of an elevator installation.
[0006] The object of the present invention is achieved by a method, apparatus and computer program as defined by the respective independent claims.
[0007] According to a first aspect, there is provided a method for generating data describing the quality of an elevator installation, the method being performed by an apparatus configured to execute a machine learning model trained with simulation data of a simulation model corresponding to an installed elevator for evaluating the quality of the elevator installation, the method comprising:
[0008] receiving data describing the operation of at least one entity of the installed elevator, the data being generated with a test procedure of the at least one entity of the installed elevator,
[0009] inputting the received data to the machine learning model executed by the apparatus,
[0010] According to the output from the machine learning model, a detection result is set to express one of: (i) the quality of the elevator installation is acceptable, (ii) the quality of the elevator installation is not acceptable.
[0011] The data describing the operation of at least one entity of the installed elevator can be received from at least one of: a driver of an elevator door, an accelerometer associated with an elevator car, a magnetometer associated with an elevator car, a microphone, an image sensor, a depth sensor.
[0012] The method can further comprise:
[0013] In response to the detection result being set to express that the quality of the elevator installation is not acceptable, a signal is generated to a terminal device communicatively connected to the apparatus, the generated signal carrying data indicative of the detection result set to express that the quality of the elevator installation is not acceptable.
[0014] Further, the data carried in the generated signal can also define at least one entity causing the quality of the elevator installation to be not acceptable. Further, the data carried in the generated signal can further define one or more instructions to overcome the situation expressing that the quality of the elevator installation is not acceptable.
[0015] Still further, the method can further comprise:
[0016] In response to the detection result being set to express that the quality of the elevator installation is acceptable, a quality report is generated to a predefined entity, the quality report comprising data indicative of the detection result set to express that the quality of the elevator installation is acceptable.
[0017] According to a second aspect, there is provided an apparatus for generating data describing the quality of an elevator installation, the apparatus being configured to execute a machine learning model trained with simulation data of a simulation model corresponding to an installed elevator for evaluating the quality of the elevator installation, the apparatus further being configured to:
[0018] receive data describing the operation of at least one entity of the installed elevator, the data being generated with a testing procedure of the at least one entity of the installed elevator,
[0019] input the received data to the machine learning model executed by the apparatus,
[0020] According to the output from the machine learning model, a detection result is set to express one of: (i) the quality of the elevator installation is acceptable, (ii) the quality of the elevator installation is not acceptable.
[0021] The apparatus can be configured to receive data describing operation of at least one entity of the installed elevator from at least one of: a drive of an elevator door, an accelerometer associated with the elevator car, a magnetometer associated with the elevator car, a microphone, an image sensor, a depth sensor.
[0022] The apparatus can be further configured to:
[0023] in response to the detection result being set to indicate that the quality of the elevator installation is not acceptable, generate a signal to a terminal device communicatively connected to the apparatus, the generated signal carrying data indicative of the detection result set to indicate that the quality of the elevator installation is not acceptable.
[0024] Further, the data carried in the generated signal can also define at least one entity causing the quality of the elevator installation to be not acceptable. Further, the data carried in the generated signal can further define one or more instructions to overcome the situation indicating that the quality of the elevator installation is not acceptable.
[0025] Still further, the apparatus can be further configured to:
[0026] in response to the detection result being set to indicate that the quality of the elevator installation is acceptable, generate a quality report to a predefined entity, the quality report comprising data indicative of the detection result set to indicate that the quality of the elevator installation is acceptable.
[0027] According to a third aspect, there is provided a computer program comprising instructions causing the apparatus of the second aspect as defined above to perform the steps of the method according to the first aspect as defined above.
[0028] The expression “several” means herein any positive integer from one, such as two or three.
[0029] The expression “a plurality” means herein any positive integer from two, such as two, three or four.
[0030] The various exemplary and non-limiting embodiments of the present application, both as to their construction and their method of operation, together with additional objects and advantages thereof, will be best understood from the following description of specific exemplary and non-limiting embodiments when read with reference to the accompanying drawings.
[0031] The verbs “comprise” and “include” are used as open-ended limitations in this document, both in reference to the claims and describing the structure of those items. The BRIEF DESCRIPTION OF DRAWINGS
[0032] In the drawings of the figures, embodiments of the application are shown by way of example and not limitation.
[0033] Figure 1 A system according to an example is schematically illustrated.
[0034] Figure 2 A method according to an example is schematically illustrated.
[0035] Figure 3 Another aspect related to a method according to an example is schematically illustrated.
[0036] Figure 4 Yet another aspect related to a method according to an example is schematically illustrated.
[0037] Figure 5 An apparatus according to an example is schematically illustrated. DETAILED DESCRIPTION
[0038] The specific examples provided in the description below should not be interpreted as limiting the scope and / or applicability of the claims. Rather, the examples provided in the description below are illustrative of specific ways in which the claims can be practiced.
[0039] The present application is used for assessing the quality of an elevator installation, and the object is to generate data describing the quality of the elevator installation. The elevator installation corresponds to the situation where a new elevator is built and the object is to put it into use. In addition, it corresponds to the situation where a maintenance operation is performed on an existing elevator, where the maintenance operation requires testing the elevator in one or more predetermined ways. The maintenance operation can refer to replacement of one or more entities of the elevator that require installation work, which also includes any software installation or reinstallation and any replacement of physical components or systems of the elevator. Any combination of the described elevator installation falls within the scope of the present application.
[0040] In the upcoming description, at least some aspects of the present application are described by referring to Figure 1 Figure 1 A system according to an example is schematically illustrated. Figure 1 At least the following entities of the elevator system are shown: an elevator controller 110, an elevator drive system, a traction sheave with an electric motor 130, an elevator car 140, an elevator door system 150, a plurality of sensors 160. In addition, Figure 1 A device 170 is schematically illustrated, which can be considered as an assessment unit of the data input to it. In Figure 1 a non-limiting example, the device 170 is associated with the elevator car 140, but it can reside in the elevator controller 110 or in the elevator drive system. Figure 1 another location different from the shown location. Alternatively or in addition, in at least some embodiments, the functionality of the device 170 can be integrated to one or more other entities, for example to the elevator controller 110 or to a device not physically associated with the elevator car 140.
[0041] As to the device 170, it is to be considered as a computing device arranged to receive a predefined type of data as input. As a non-limiting example, the data can for example refer to sensor data received e.g. from the sensor system 160. The device 170 can also comprise one or more internal sensors for collecting data to be analyzed with the device 170 in addition to or instead of data from the sensor system 160. The device 170 comprises a communication interface through which the device 170 can be communicatively connected to other entities, for example to the sensor system 160, but also to other entities, for example to the elevator controller 110 or to the elevator drive 120 or the like. The communication technology used in the communication can be a wired communication technology or a wireless communication technology or any applicable combination of these technologies as known from the prior art.
[0042] Further, the device 170 is configured to execute a machine learning model trained with simulation data of a simulation model, which refers to a virtual elevator model, also called digital twin, corresponding to the elevator under installation, for evaluating the quality of the elevator installation. In other words, the simulation data can be used to train the machine learning model with simulation data of a simulation model corresponding to the elevator under installation. Figure 1The computing system denoted with 180 is generated, which is configured to simulate the operation of the elevator in various ways with a simulation model. The simulation model is to be understood herein to correspond to an elevator installed with a predetermined accuracy. The accuracy can be defined on the entire elevator system level or entity level depending on the installation operation performed on the elevator in question. The accuracy can be, for example, at the level of the simulation model corresponding exactly to the elevator in the installation, or it can belong to the same product line, etc. In any method, the simulation model is to be such that the simulation data generated with the simulation model is applicable in the context of the elevator installed at the required level. In connection with the simulation, the simulation model is executed with various input parameters and in any other way in order to generate training data to train the machine learning model executed by the device 170 in its operation. For example, the simulation model can be executed with parameters that make the simulation model follow the normal operation of the elevator. In addition, the simulation model can be executed with parameters that make the simulation model end various error situations, which can be considered to correspond to a malfunction of the elevator to which the simulation model corresponds. Thus, the training data generated by simulating the simulation model can be considered to comprise data describing acceptable operation of the elevator and data describing unacceptable operation of the elevator. In some embodiments, the training data can define only one of the mentioned operations, i.e. the data describes acceptable operation of the elevator, or the data describes unacceptable operation of the elevator. In such a method, the executing entity of the machine learning model can be configured to execute such that if the trained machine learning model does not detect a state, i.e. the type of operation of the elevator, it is trained to detect, it can conclude that it generates a result indicating another state (see acceptable operation / unacceptable operation).
[0043] The training of the machine learning model can be executed in the computing system 180 with the generated training data. In response to the training, the trained machine learning model can be transferred to the device 170 to be executed on site. The transfer of the trained simulation model to the device 170 can be arranged by the applied communication channel or by transferring the model with a medium suitable for storing data for this purpose, where the medium can be, for example, a transferable data memory, such as a memory stick. Alternatively or additionally, the training of the machine learning model can be executed in the device 170, such that the training data from the computing system 180 is transferred therein and the training is executed by the device 170. As derivable from the above, the machine learning model can be trained to perform a classification task on the data input thereto in order to decide, on the basis of the data input to the machine learning model, whether the elevator is operated in an acceptable manner or in an unacceptable manner.
[0044] Depending on the implementation of the machine learning model and the format of the training data, the machine learning model can be configured to output, in addition to the detection result described above, data identifying at least one entity of the elevator, such that the detection result corresponds to an unacceptable operation of the elevator. This approach can require that the training data also defines the entity that causes the detection result describing an unacceptable operation of the elevator, which is then also generated as an output in the internal operation of the machine learning model, for example together with or integrated into the detection result. Naturally, in the case of detecting that the elevator is operating in an acceptable manner, corresponding additional data can also be generated or associated.
[0045] From an operational point of view, the apparatus 170 or another suitable entity can be arranged to perform at least the method as illustrated in Figure 2 by applying the machine learning model trained as described in this method in a manner as set out in the upcoming description. By performing this method, data describing the quality of the elevator installation can be generated. First, in Figure 2In the step denoted with 210, data describing the operation of at least one entity of the installed elevator is received 210. In other words, the data describing the operation of at least one entity can refer to data obtained from the sensor system 160 with one or more sensors, and the data can represent the overall operation of the elevator under installation or the operation of one or more predefined entities of the elevator. Alternatively, the data can be received 210 by obtaining predefined operational parameters from the elevator, such as control signal data from the elevator drive 120, etc. In particular, the data received by the device 170 is generated with a test procedure of at least one entity of the installed elevator. This refers to the fact that due to the installation work, the elevator or at least one relevant entity of the elevator is tested in a predetermined manner in accordance with the installation tasks performed on the elevator. The test procedure can for example correspond to a test drive of the elevator, i.e. the elevator car 140 is made to travel in its path in a predefined manner in accordance with a test plan. Furthermore, the test plan can correspond to operating one or more predefined entities and monitoring their operation, for example, with sensors. For example, if the installation relates to the replacement of elevator doors (see the landing doors and / or the elevator car doors), the test procedure can include making the elevator doors open and close a predetermined number of times, for example, in one or more floors, and data from the door drive, for example, is received by the device 170. For the sake of completeness, it is worth mentioning that the sensors applied in the context of the present invention can be of different types, such as accelerometers or magnetometers associated with moving parts of the elevator system, such as the elevator car 140. Other examples of applicable sensors can be microphones, image sensors, depth sensors, which can be used to capture various types of data describing the operation of at least one entity of the elevator under installation, for example, indicating the gap between two or more entities of the elevator. With the various arrangements of generating data transmitted to the device 170, the operating conditions of multiple entities of the elevator can be evaluated. For example, such operating conditions can be one of the following aspects, for example: misalignment of each landing door roller, door lock gap anomaly, misalignment of the guide rail, mechanical shortcut between the car and the pulley beam, mechanical shortcut between the machinery and the guide rail, incorrectly installed guide shoe or guide rail (without continuous lubrication), etc. In the foregoing description, it is mainly described that the data is received by the device 170 from the sensor system 160, but as mentioned above, the data source can be different from the sensor system 160. Furthermore, the device 170 can be configured to receive data directly from the respective source or through another entity, for example, through the elevator controller 110 or similar. Thus, the test procedure can be controlled directly or indirectly from the device 170, or it can be triggered, for example, by the elevator controller 110, which is configured to collect data, for example, and transmit it to the device 170.
[0046] In response to receiving data obtainable e.g. from a predetermined test procedure performed by the installed elevator, the received data is input 220 to a machine learning model executed by the device 170. The machine learning model is configured to output a detection result by evaluating the data input 220 thereto to express, based on the received data, whether the elevator is operating as expected. Thus, in dependence of the output from the machine learning model, the device 170 is configured to set 230 the detection result to express one of: (i) the quality of the elevator installation is acceptable 230A, (ii) the quality of the elevator installation is not acceptable 230B.
[0047] Generating data describing the quality of the elevator installation in the manner described in the foregoing description can continue as Figure 3 illustrated in Fig. 3. That is, in response to the detection result being set to indicate that the quality of the elevator installation is not acceptable 230B, a signal can be generated 310 to a terminal device 190 in communication connection with the device 170. The generated signal can comprise data indicative of the detection result set to indicate that the quality of the elevator installation is not acceptable 230B. The terminal device 190 here can refer to a terminal device of a technician, e.g. residing at the site where the elevator is installed. For example, the technician can be the technician who has performed the installation task and who has triggered the test procedure of at least one entity of the elevator. Thus, in response to the generation 310 of the signal to the terminal device, the result in case of an unacceptable installation quality can be delivered to the technician. The communication connection can be implemented with a suitable communication technology, such as a wireless near field communication technology, like Wi-Fi or Bluetooth.
[0048] According to embodiments of the present application, in Figure 3The signal generated in step 310 of the method can also carry data further defining the at least one entity causing the unacceptable 230B quality of the elevator installation. Such an embodiment can be implemented so that the machine learning model is also trained to return data indicating one or more entities causing the unacceptable 230B quality of the elevator installation, which information is then transmitted by the device 170 to the terminal device 190. The terminal device 190 can then output information to the technician that helps the technician to take the necessary measures regarding the root cause of the detection result indicating the unacceptable 230B quality in the installation. In some further embodiments, the transmitted data can also comprise one or more instructions to overcome the situation representing the unacceptable quality of the elevator installation, for example describing the necessary tasks the technician should take to overcome the unacceptable quality. The data can also be provided by the machine learning model when trained to do so, or it can be included by the device 170 in the signal transmitted to the terminal device 190. The device 170 can for example store such data in an internal memory, or it can query it from an external memory it is arranged to access. In case the data is fetched from a memory, the machine learning model can be arranged to return data describing the data to be fetched from the memory, such as an indicator value of the data or a memory or network address of such data. Another option to arrange the technician to access the data can be that the device 170 is configured to include a network address to the signal transmitted to the terminal device 190 to enable the technician to access the data in an easy way.
[0049] Further, in case the detection result represents an acceptable quality of the elevator installation, the device 170 can be configured to perform further operations. Such method steps are schematically shown in Figure 4 . Namely, according to one embodiment, the device 170 can be configured to generate 410 a quality report to a predefined entity in response to the detection result being set to represent that the quality of the elevator installation is acceptable. The predefined entity can for example be the terminal device 190 of the technician or a computing entity of the maintenance company performing the installation task or a computing entity of the party owning the elevator under installation, or any combination of these. The quality report can for example comprise data describing the test procedure, such as any analysis results of the received data, and the raw data. Such a method enables proper documentation of the elevator installation procedure for any further use, for example for verification if necessary.
[0050] It is also worth mentioning that the present application according to some embodiments can be configured to perform only one of the method steps disclosed in Figure 3 and Figure 4 , or both of them depending on the detection result.
[0051] As already mentioned, the apparatus 170 configured to perform the method can be a device that can be associated with an elevator, for example a device associated with the elevator car 140 already mentioned. In some other embodiments, the functionality of the apparatus 170 described can be integrated to another entity, for example an entity belonging to the elevator system. Non-limiting examples of other entities can be, for example, the elevator controller 110.
[0052] Figure 5 An example of an apparatus adapted to perform the method is schematically illustrated in Fig. 2. Thus, Figure 5 The apparatus of Fig. 1 can be configured to perform the generation of data describing the mass of the elevator device. For the sake of clarity, it is worth mentioning that, Figure 5 The block diagram of Fig. 1 depicts some components of an entity that can be used to implement the functionality of the apparatus 170. Figure 5 The apparatus of Fig. 1 comprises a processor 510 and a memory 520. The memory 520 can store data, such as the data pieces described, but also computer program code 525 causing operations in the described manner. The apparatus can further comprise a communication interface 530, such as a wireless communication interface or a communication interface for wired communication, or both, to communicate with other entities described. Thus, the communication interface 530 can comprise one or more modems, antennas, and any other hardware and software to enable communication, for example under the control of the processor 510. Furthermore, an I / O (input / output) component can be arranged with the processor 510 and a part of the computer program code 525 to provide a user interface for receiving input from a user, such as from a technician, and / or providing output to a user of the apparatus, if necessary. In particular, the I / O component can comprise a user input apparatus, such as one or more keys or buttons, a keyboard, a touch screen or a touch pad, etc. The I / O component can comprise an output apparatus, such as a loudspeaker, a display or a touch screen. The components of the apparatus 170 can be communicatively connected to each other via a data bus, which enables transmission of data and control information between the components.
[0053] The memory 520 and at least a part of the computer program code 525 stored therein can also be arranged, together with the processor 510, to cause the apparatus to perform at least a part of the method as described herein. The processor 510 can be configured to read from and write to the memory 520. Although the processor 510 is depicted as a respective single component, it can be implemented as one or more separate processing components accordingly. Similarly, although the memory 520 is depicted as a respective single component, it can be implemented as one or more separate components accordingly, some or all of which can be integrated / removable and / or can provide permanent / semi-permanent / dynamic / cached storage.
[0054] The computer program code 525 can comprise computer executable instructions, which, when loaded into the processor 510 of the respective apparatus 170, implement the functions corresponding to the steps realized in the method. As an example, the computer program code 525 can comprise a computer program consisting of one or more sequences of one or more instructions. The processor 510 is able to load and execute the computer program by reading the one or more sequences of one or more instructions included in the computer program from the memory 520. The one or more sequences of one or more instructions can be configured to, when executed by the processor 510, cause the apparatus, such as a computer, to perform the described method. Thus, the apparatus can comprise at least one processor 510 and at least one memory 520 including the computer program code 525 for one or more programs, the at least one memory 520 and the computer program code 525 configured to, with the at least one processor 510, cause the apparatus to perform the method.
[0055] The computer program code 525 or at least some of its parts, e.g. a computer program product comprising at least one computer-readable non-transitory medium having stored thereon the computer program code 525, can be provided, which, when executed by the processor 510, cause the apparatus to perform the method. The computer-readable non-transitory medium can comprise a memory device or a recording medium, such as a CD-ROM, a DVD, a Blu-Ray disk, or another article of manufacture that tangibly embodies the computer program. As another example, the computer program can be provided as a signal configured to reliably transfer the computer program.
[0056] Further, the computer program code 525 can comprise a proprietary application, such as a computer program code for causing the execution of the method in the manner as described in the description herein.
[0057] Any of the programming functions mentioned can also be performed in firmware or hardware, as is appropriate.
[0058] For the sake of completeness, it is worth mentioning that the entity performing the method in the role of the apparatus 170 can also be implemented with a plurality of apparatuses, such as Figure 5 the apparatus schematically illustrated in Fig. 1 as a distributed computing environment corresponding to the apparatus. For example, one of the devices can be communicatively connected with the other devices and share, for example, data of the method, to cause another device to perform at least one other part of the method. As a result, the method performed in the distributed computing environment generates control signals indicative of the allocation of responsibilities as described.
[0059] The specific examples provided in the description above should not be construed as limiting the applicability and / or interpretation of the appended claims. Lists and groups of examples provided in the description above are not exhaustive unless otherwise specifically stated.
Claims
1. A method for generating data describing the quality of elevator equipment, the method being performed by an apparatus (170) configured to execute a machine learning model trained using simulation data from a simulation model corresponding to an installed elevator, the method comprising: Receive (210) data describing the operation of at least one entity of the installed elevator, said data being generated using a testing process of said at least one entity of the installed elevator. The received data is input (220) into a machine learning model executed by the device (170). Based on the output from the machine learning model, the detection result is set to indicate one of the following: (i) the quality of the elevator equipment is acceptable (230A), (ii) the quality of the elevator equipment is unacceptable (230B).
2. The method of claim 1, wherein data describing the operation of the at least one entity of the installed elevator is received from at least one of: an elevator door actuator, an accelerometer associated with the elevator car, a magnetometer associated with the elevator car, a microphone, an image sensor, and a depth sensor.
3. The method according to any one of the preceding claims, further comprising: In response to the detection result being set to indicate that the quality of the elevator equipment is unacceptable, a signal is generated to a terminal device communicatively connected to the device (170), the generated signal carrying data indicating the detection result being set to indicate that the quality of the elevator equipment is unacceptable.
4. The method according to claim 3, wherein, The data carried in the generated signal also defines at least one entity that causes the quality of the elevator equipment to be unacceptable.
5. The method according to claim 4, wherein, The data carried in the generated signal also defines one or more instructions to overcome situations indicating that the quality of the elevator equipment is unacceptable.
6. The method according to any one of the preceding claims, further comprising: In response to the test results being set to indicate that the quality of the elevator equipment is acceptable, a quality report is generated for a predefined entity, the quality report including data indicating that the test results are set to indicate that the quality of the elevator equipment is acceptable.
7. An apparatus (170) for generating data describing the quality of elevator equipment, the apparatus (170) being configured to execute a machine learning model trained using simulation data from a simulation model corresponding to an installed elevator for evaluating the quality of the elevator equipment, the apparatus (170) further being configured to: Receive (210) data describing the operation of at least one entity of the installed elevator, said data being generated using a testing process of said at least one entity of the installed elevator. The received data is input (220) into a machine learning model executed by the device (170). Based on the output from the machine learning model, the detection result is set to indicate one of the following: (i) the quality of the elevator equipment is acceptable (230A), (ii) the quality of the elevator equipment is unacceptable (230B).
8. The apparatus (170) according to claim 7, wherein, The device (170) is configured to receive data describing the operation of at least one entity of the installed elevator from at least one of the following: an elevator door actuator, an accelerometer associated with the elevator car, a magnetometer associated with the elevator car, a microphone, an image sensor, and a depth sensor.
9. The apparatus (170) according to claim 7 or claim 8, wherein the apparatus (170) is further configured to: In response to the detection result being set to indicate that the quality of the elevator equipment is unacceptable, a signal is generated to a terminal device communicatively connected to the device (170), the generated signal carrying data indicating the detection result being set to indicate that the quality of the elevator equipment is unacceptable.
10. The apparatus (170) according to claim 9, wherein, The data carried in the generated signal also defines at least one entity that causes the quality of the elevator equipment to be unacceptable.
11. The apparatus (170) according to claim 10, wherein, The data carried in the generated signal also defines one or more instructions to overcome situations indicating that the quality of the elevator equipment is unacceptable.
12. The apparatus (170) according to any one of claims 7 to 11, wherein the apparatus (170) is further configured to: In response to the test results being set to indicate that the quality of the elevator equipment is acceptable, a quality report is generated for a predefined entity, the quality report including data indicating that the test results are set to indicate that the quality of the elevator equipment is acceptable.
13. A computer program comprising instructions for causing the apparatus (170) according to claim 7 to perform the steps of the method according to any one of claims 1 to 6.