Method and system for commissioning diagnostics of passenger conveyor system components

Abstract

The invention relates to a method and a system for commissioning a passenger conveyor system after installation of at least one component. The system comprises a diagnostic unit (32) and a control system configured to operate the system, and the method comprises automatically performing the following steps for auditing the operation of the installed components by means of the computing unit before bringing at least one installed component into normal use. Starting (40) a run sequence of the operation of the installed components; acquiring (42) receivable parameterized measurement data relating to the condition and / or operation of the installed system components during the run sequence; transferring (44) the measurement data to the diagnostic unit (32); setting (46) one or more limit values for the acquired parameterized measurement data and thereby defining a desired performance range of the components; determining by the diagnostic unit that the acquired measurement data extends the defined performance range to an extent beyond said one or more limit values; and issuing (48) an electronic report indicating the result of the determination.

Description

Technical Field

[0001] This invention relates to the field of passenger conveyor systems, such as elevator systems, escalator systems, or moving walkways (i.e., moving sidewalks), which are composed of several components and subsystems. In particular, this invention relates to the commissioning of these components after their installation. Specifically, this invention relates to a method for checking the functionality and operational readiness of at least one component or at least one subsystem of such a passenger conveyor system after installation. Background Technology

[0002] Any of these passenger transport systems can generally be considered a distributed electromechanical system. Taking an elevator as an example, it includes an elevator car and possibly a counterweight, which are suspended in the elevator shaft by a suspension device or supported by any hydraulic mechanism. Numerous mechanical and electrical components belong to this type of system, especially those arranged along the elevator shaft. All these elevator components contribute to a variety of elevator functions, such as guiding any elevator car movement within the elevator shaft, measuring the elevator car position, illuminating the elevator system, suspending the elevator car, driving the elevator car, controlling the elevator system, transmitting data within and / or to entities outside the elevator system, interfacing with elevator users, ensuring the safety of the elevator system, and braking and / or stopping the elevator car movement. Among these elevator components are elevator doors, door drive units, guide rails, fixed supports, traction ropes, traction sheaves, motors, encoders, bearings, lights, control nodes, user interfaces, data cables, cameras, position measurement targets for measuring the elevator car position, safety switches and safety contacts, safety brakes, control cabinets, power units, and various other types of sensors, actuators, and structural components.

[0003] Traditionally, after elevator installation, functional behavior verification is performed to ensure the elevator operates as expected. This testing typically involves verifying that the elevator can reach all floors at the desired speed and with the expected ride comfort, and that the doors can open, allowing safe access to the elevator car from each floor.

[0004] For example, when elevator doors are assembled and connected to the shaft wall or landing door openings, the drilling, positioning, and alignment of components are often done manually, leading to variations in the quality of door operation. Although installation checks are performed to determine if newly installed door components are within their intended functional range, the latter still manifests in the aforementioned variations in door operation due to the different installation conditions that must be accepted, as each component is somewhat different.

[0005] After the basic functions have been approved by technicians, the elevator system is put into operation, and then maintenance monitoring begins, which assesses when components need maintenance or replacement. For example, document US6439350B1 discloses a maintenance method for checking the operation of an elevator door system, wherein operating parameters of the door system are measured, and a maintenance message is sent if the operating parameters exceed a threshold.

[0006] Other passenger transport systems, such as escalators or moving walkways, even when specific to a single system, typically include numerous mechanical and electrical components, arranged along the length of the escalator or moving walkway. All these components contribute to various operational functions, such as guiding the movement of steps or trays. Other components include handrails, step chains, drive chains, guide rails, handrails, comb plates, traction systems, motors, encoders, lights, control nodes, user interfaces, data cables, cameras, position measuring devices, safety switches and contacts, safety brakes, control cabinets, electrical units, and various types of sensors, actuators, and structural elements. When commissioning such systems after installation, functional behaviors are performed to ensure the system operates as expected. Typically, this test operation includes verifying that the system can operate at the desired speed and with the comfort of the passengers, and that the steps or trays move seamlessly.

[0007] For example, when assembling and attaching comb plates or railings to a structure, the drilling, positioning, and alignment of components are often done manually, leading to variations in the quality of component operation. Although installation checks are performed to confirm that newly installed components are within functional limits, the system may still exhibit operational variability due to differences in installation conditions.

[0008] After the technicians approve the basic functions, the system is put into operation and maintenance monitoring begins. This process assesses when components need maintenance or replacement. Summary of the Invention

[0009] Purpose of the Invention. The purpose of embodiments of the present invention is to overcome the aforementioned deficiencies. Specifically, the purpose of embodiments of the present invention is to improve the installation quality of components by improving the commissioning of the components before putting them into normal use.

[0010] The aforementioned objectives are achieved by the subject matter of independent claims 1, 9, 10, 11, and 13, which respectively disclose a method for commissioning at least one component or at least one subsystem of a passenger conveyor system after installation, a method for installing and commissioning a passenger conveyor, a computer program for implementing the steps of the method, and a passenger conveyor system. The dependent claims relate to advantageous embodiments.

[0011] The disclosed invention overcomes the limitations of the prior art by providing a method for qualitatively assessing the condition and / or operation of installed system components before their normal use. The applied measurements establish a benchmark for quality checks of the installed components, as this check may be conducted during subsequent maintenance analysis after the components have been in normal operation for a period of time. By applying methods for measuring component condition and operational quality, operational margins—that is, the extent to which the condition may deteriorate before a functional failure—can be assessed before normal use of the components.

[0012] According to embodiments of the present invention, several components can also be debugged together in one or more units, so that the debugging method of the present invention can be applied to the units in batches and continuously.

[0013] Regarding the aforementioned components as examples, such as elevator doors, door drive units, guide rails, fixed brackets, traction ropes, traction sheaves, motors, encoders, bearings, lights, control nodes, user interfaces, data cables, cameras, position measuring targets for measuring the position of the elevator car, safety switches and safety contacts, safety brakes, control cabinets, power units, handrails, step chains, drive chains, railings, comb plates, traction systems, and various other types of sensors, actuators, and structural components, there are components present that can already provide data recording itself, because the sensors are mounted together with the corresponding components. In the absence of other components with devices for providing data for the method according to the invention, sensors can advantageously be used and coupled to the corresponding components, said sensors enabling data to be read out separately when the corresponding components are in operation.

[0014] This method utilizes an acquisition unit to collect decisive data, a diagnostic unit communicatively coupled to the components and configured to monitor the installed components, and a control system responsible for forwarding results and initiating passenger conveyor system operation, during which test data can be collected. If the installed component belongs to and is assigned to a subsystem of the passenger conveyor system, the diagnostic unit can query the corresponding operational capabilities via that subsystem. Therefore, the diagnostic unit resides at a higher level than the various subsystems with which it can communicate. The proposed method includes a series of steps that analyze and verify the performance of the components against set constraints, the results of which are transmitted as digital publishing reports.

[0015] According to the present invention, the sequence of steps is executed by a computer program that serves as an analysis tool for debugging at least one component of a passenger conveyor system.

[0016] According to a convenient embodiment, these steps are performed automatically without the intervention of technicians or assembly workers. At the very least, this helps technicians achieve the initial stage. If the initial stage shows very good results, the installation process can be completed quickly. If the initial results of the automated execution are not acceptable, the technician can refine the instructions with the help of their intervention on the command computer.

[0017] This invention provides a method for numerically estimating installation quality, allowing installers to detect and correct problems on-site before completing the installation process. This results in passenger conveyors being in better working order, significantly reducing the "first-year repair rate" and minimizing unnecessary on-site visits.

[0018] In addition, the commissioning report is automatically generated and can display the results of the analysis, such as highlighting potential correction actions (e.g., alignment adjustments) when needed, and enabling the rapid reassessment of installation quality using new commissioning sequences.

[0019] With advancements in passenger conveyor diagnostics, modern systems generate vast amounts of data that can be used to monitor the condition of subsystems and components. It is precisely this data that is used according to the present invention to analyze the condition of installed components in the early stages of installation. For example, the average friction of the door system, including the car and landing doors, can be quantified, allowing for the measurement of operational margins during commissioning, i.e., "day 0" before the elevator is put into normal operation. By analyzing this data, corrective actions, such as realigning the landing doors, can be taken before final delivery for normal use.

[0020] Following the mechanical installation of the component under consideration, i.e., after its installation, the component must enable any data exchange by electrically and / or electronically connecting it to the main system or its subsystems. In this regard, it must be assumed that the passenger conveyor system is not actually in normal operating mode during the installation of the new component.

[0021] When the method is initiated by activating the corresponding application on the computing unit, the foregoing steps will first involve checking any authorization from the requester. Such authorization includes obtaining permission, at least to a certain extent, to control the passenger conveyor system in order to perform the method of the invention.

[0022] Following a successful authorization process, the method then involves a series of automated steps performed by a computing unit before the passenger conveyor system components are put into normal operation. These automated steps are performed communicatively with the system control unit. The computing unit can therefore be a unit of the controller. However, it can also be a computing unit located in a network (cloud) that instructs the passenger conveyor system controller to perform the following steps externally: Startup Operation Sequence: This method begins with a start command and triggers a sequence of operations for the installed components by controlling the components to perform at least one of their functions under controlled conditions. During this phase, the system activates the components used to acquire the measurement results to be obtained by the acquisition unit.

[0023] Controlled conditions can deviate from the operating mode of a component under normal use, offering the advantage of being able to run specific test sequences. Compared to tests conducted solely under normal operation, these can provide better and more detailed information about the component's functionality through the specific measurements obtained. According to one embodiment, the operating sequence for operating the component under consideration in a specific manner may further include embedding the operating sequence by coupling it with other entities.

[0024] For example, the function of opening and closing elevator doors can be embedded in the process of the elevator car reaching a specific floor. Measurement data for door movement is then used to evaluate the door's functionality to adjust the door or a part of the door; this measurement data for door movement is embedded in the process of car movement, which ends as the car reaches the specific floor.

[0025] Data Acquisition: As a component operates through its operational sequence, parameters are calculated using collected measurement data related to the component's condition or operational function. This data may include measurements such as friction levels, load variations, vibration, noise, or other key performance parameters. Easily understandable results can be calculated from these parameters. These parameters vary individually for each component and must be selected and specified for commissioning. Therefore, the program must include instructions for selecting performance parameters.

[0026] Setting the performance range: The control system sets one or more limit values ​​for the acquired data and / or parameters. These limits establish the desired performance range, which is a benchmark representing the acceptable operating conditions of the component. By setting these values, the system can detect deviations that may indicate potential problems in alignment, installation quality, or component wear.

[0027] There are several possibilities for finding such a performance range: as an alternative, in addition to possibly testing whether the parameter value is within the “predetermined” range where its limits are considered “functionally acceptable” and which limits are stored, each individual measurement can be evaluated to determine how much it deviates from the average of the actual set of all measurements.

[0028] For example, it is possible that the actual measured value or parameter value still falls within the "functionally acceptable" range defined above, but exhibits singularity due to a significant deviation from all other actual values ​​or the average of the actual dataset.

[0029] This method involves not only assessing absolute compliance with predetermined limits but also statistical analysis to identify outliers or anomalies. Such anomalies can signal a problem even if the parameter in question has not exceeded acceptable operational limits. By comparing individual measurements with a collective dataset (including the mean, median, or other statistical indicators), the system can detect inconsistent or irregular patterns.

[0030] For example, parameters may remain within functional limits but exhibit sustained or sudden spikes that are significantly different from observed typical values. This could indicate underlying wear, misalignment, or other issues within the system that may not yet have reached critical limits but could degrade performance or lead to failure if left unaddressed.

[0031] This method of identifying deviations from average or expected behavior adds an extra layer of reliability and predictability to the monitoring process, enabling the early detection of subtle trends and the improvement of predictive maintenance strategies. It ensures that outliers or anomalies are flagged for further investigation, even if they remain within conventional operating limits.

[0032] The mathematical implementation of this concept can be based on statistical methods that analyze the deviation of individual measurements from the mean or other reference values. This could be based, for example, on first calculating the average (mean) of all measurements, which represents the central or typical value of the dataset. Next, the variation or spread of the measurements (standard deviation) can be determined, showing how far each value deviates from the mean on average. Small variations indicate that the measurements are closely grouped, while larger variations indicate more significant differences.

[0033] Each individual measurement is then analyzed to determine its deviation from the mean. This deviation is compared to typical variations to identify whether the value is abnormal. An acceptable range is defined as "normal" or "functionally acceptable," and values ​​falling within this range are considered problem-free. However, values ​​outside this range are flagged as potential problems.

[0034] Even if a value is within an acceptable range, it may still deviate significantly from other values. In such cases, it is identified as an outlier or singularity. This helps in the early detection of potential problems, even if the value has not yet exceeded the predetermined acceptable limits. This method provides a reliable way to identify anomalies and ensure accurate monitoring.

[0035] Limit values ​​and / or performance ranges can be generated by or assisted by a learning algorithm supported by AI processing running on a computing unit using trained machine learning. This computing unit can be part of a controller in the system field. Alternatively, the algorithm processing can be performed on an external computing unit, with the passenger conveyor system's diagnostic unit (see below) communicatively connected to it. According to another solution, limit values ​​can be listed in the datasheet of the component to be installed, where the corresponding data is automatically loaded when the component is installed and coupled to the passenger conveyor system's control and diagnostic units.

[0036] Data is transmitted to the diagnostic unit: Measurement data and / or calculated parameters, along with established limits and defined performance ranges, are transmitted to the diagnostic unit. The diagnostic unit is typically located in the field of the device and is responsible for edge processing tasks such as sampling raw sensor data, performing statistical calculations, and filtering noise.

[0037] Degree Determination: Using a diagnostic unit, this method determines the extent to which measured data are within or exceed predefined performance limits. This step allows the system to detect whether the condition or performance of a component deviates from acceptable thresholds.

[0038] According to the diagnostic system of this invention, a fundamental interactive relationship exists between the diagnostic unit and the network cloud for monitoring and analyzing the performance of any system. The diagnostic unit, as described above, is typically located at the device site. Meanwhile, the network cloud provides robust computing resources for higher-level analysis, such as determining whether a system meets or fails certain performance criteria based on processed data.

[0039] In the standard configuration, the diagnostic unit handles the initial processing steps. It collects raw sensor data, such as current values ​​or encoder pulse signals. The unit then extracts relevant measurement parameters, such as force or position, and refines them into more meaningful condition data, such as friction, using a predefined algorithm. This performs edge processing and can be expanded to aggregate or average these values ​​(e.g., average friction). The refined data can then be communicated to a network cloud for advanced analytics. The cloud evaluates the data to determine outcomes, such as whether the system is functioning correctly or has malfunctioned. For example, the cloud might report: "Failure! Layer 5, Side A: Excessive Friction." Such a solution is similar to a hybrid configuration where preprocessing is performed locally on the diagnostic unit, and the processed data is transmitted to the cloud for analysis. This setup minimizes the amount of data sent over the network, reducing bandwidth usage, while maintaining the cloud's advanced analytics capabilities.

[0040] However, outside this standard setup, various configurations can distribute these processing and analysis tasks between the diagnostic unit and the network cloud. This flexibility in combination provides opportunities to tailor system functionality for specific use cases: in one scenario, analysis can be performed entirely on the diagnostic unit at the device site. This approach is suitable for systems with limited network connectivity or where real-time, on-site decision-making is critical. For example, the diagnostic unit could calculate excessive friction on side A of the 5th floor and immediately trigger a local alarm. Conversely, both preprocessing steps can be offloaded to the network cloud. In this setup, raw sensor data is transmitted directly to the cloud, where all subsequent data refinement and decision-making are performed. This configuration leverages the vast computing resources and centralized data storage of the cloud, enabling more complex analysis and trend prediction. However, this approach can introduce latency and relies on reliable connectivity.

[0041] Report Generation: Based on this analysis, the diagnostic unit generates an electronic report indicating the extent to which the acquired data matches the expected performance range. This report provides a detailed summary of the debugging results, effectively confirming or identifying necessary corrective actions.

[0042] This section can also provide information on past and current issues with the passenger conveyor system, such as recurring failures, signs of wear, and previous repair and maintenance activities. The aim is to provide technicians with a comprehensive overview of the passenger conveyor system's condition, taking into account its components. If known problems are identified, the report may also include specific recommendations for resolving these problems, including inspection components, appropriate tools, and suggested procedures. Step-by-step instructions may be added if necessary.

[0043] According to an advantageous embodiment, when installing multiple components, the entire commissioning process can be performed sequentially. The control system is capable of establishing a commissioning sequence, where commissioning of each component continues only if the measurement data of the preceding component remains within its performance range. This sequence ensures a comprehensive evaluation of the installation without leaving any components unchecked. In this context, the method according to the invention can be used for the complete installation of an entire passenger conveyor system, where all components belonging to the system are inspected step by step. This process is repeated until commissioning tests provide satisfactory results.

[0044] The commissioning of the passenger conveyor system then begins with a comprehensive definition of the system's characteristics, its operational requirements, and the user values ​​needed to optimize its parameters. This process considers several key aspects. When taking an elevator system as an example, these key aspects might include: building and equipment characteristics, such as building type, usage patterns, and the specific elevator model; any special requirements of the elevator, the building, or its users, such as the need to accommodate wheelchair-bound tenants on a specific floor or provide efficient access to a cafeteria on another floor; and customer preferences, which may prioritize performance, comfort, or environmental considerations based on their needs.

[0045] To determine the optimal parameters for the passenger conveyor system, an advanced algorithm was employed. This algorithm processes inputs derived from building and equipment characteristics, specific system and building requirements, and customer preferences. It references an extensive database, including suggested parameters and usage patterns, and adjusts the output based on the building's specific environment and its expected passenger conveyor system operation. The algorithm can also be integrated with a cloud-based simulator, which models system performance under defined parameters, settings, and usage patterns before commissioning. This simulation method is cost-effective, energy-efficient, and allows for rapid evaluation of various configurations. The results of these simulations are further validated through a physical commissioning sequence test.

[0046] The system commissioning process is largely automated, utilizing parameters defined by algorithms. This automation ensures accuracy and consistency, reducing the possibility of errors during the parameter setting phase. Following this, rigorous quality testing is performed to confirm that the passenger conveyor system is operating correctly, i.e., in optimal mode. This testing follows a predetermined sequence. In cases involving several passenger conveyors, this means evaluating each conveyor individually to ensure accuracy. If a conveyor fails the commissioning quality test, the system generates a detailed report on the test results, along with actionable recommendations for correcting any problems identified during the commissioning process. If the commissioning involves a subsystem responsible for several components, the subsystem can be tested individually for each component. Furthermore, if the commissioning involves a subsystem responsible for multiple conveyors, the subsystem can be tested individually for each conveyor.

[0047] This comprehensive passenger conveyor system commissioning method ensures that the passenger conveyor system operates with optimal efficiency and performance, meeting the building's technical requirements and the customer's unique preferences.

[0048] In addition, the reports generated by the diagnostic unit can include qualitative performance metrics derived from commissioning data, enhancing readability and ease of interpretation for installation teams and remote service units.

[0049] As mentioned above, the diagnostic unit can also communicate with the remote service unit, allowing for remote monitoring, analysis, and recording of commissioning results. This communication can be achieved through a direct connection or via the elevator controller. This capability helps improve oversight and facilitates a faster response to any installation anomalies.

[0050] In another embodiment, the method supports cloud-based processing, where certain analysis or control functions are outsourced to a cloud computing system. The cloud can further monitor the process in real time and can store, analyze, or reprocess the debugging results using advanced algorithms, including artificial intelligence (AI). This capability improves diagnostic accuracy by leveraging historical data and machine learning models to interpret component conditions with greater precision.

[0051] This invention offers several advantages over existing solutions. By automatically assessing the operational condition of each component immediately after installation, the method allows for early corrective action. This results in higher quality installations, reduces the likelihood of early failures, and minimizes the need for calibrated maintenance access once the passenger transport system is in normal operation.

[0052] Furthermore, this invention can transmit commissioning results to remote units in real time, support data-driven installation quality insights, and improve the effectiveness of on-site installation teams through targeted data-based feedback.

[0053] According to a preferred embodiment, the debugging results are stored in memory to keep a record of any test sequence and its progress for any subsequent evaluation, summarizing the operation of any component throughout its lifespan. Of course, such a report also serves as evidence of which tests were performed on the component. Any warranty can be easily verified as long as the results show that the component functions perfectly and fully.

[0054] According to one aspect of the invention, a computer program residing on a computer-readable medium includes a set of instructions configured to cause a computer unit or group of computers to perform the computer-implemented method steps described above. According to a convenient embodiment, these steps are performed automatically without intervention by a technician. At least, this assists a technician in achieving a first initial stage. If this initial stage has shown very good results, the installation process can be completed quickly. If the first results are not within acceptable limits, the program is configured to allow a technician to refine the instructions with the assistance of their intervention with the guiding computer.

[0055] On the other hand, there is also a non-transient computer-readable medium storing the aforementioned computer program, which is used to perform computer-implemented steps to audit the operation of at least one installed component through a computing unit.

[0056] The invention is first described below with a detailed example of an automatic elevator door used for commissioning an elevator as a passenger transport system. A second example is the overall installation of the elevator as a passenger transport system. As described below, the method involves a semi-automatic or fully automatic, data-driven approach to establishing a quality benchmark for the installation. This benchmark is used to verify the correct installation and function of each component (including the example automatic door and its components) to ensure the reliability of the elevator before normal use after door installation.

[0057] Assembly technicians access the commissioning program via a cloud connection interface, initiating the process with a predetermined commissioning sequence, checking data, and evaluating system performance. The commissioning sequence can be customized for different settings and can have several variations; for example, steps can be added to or removed from the sequence based on the measured condition data. The algorithm for analyzing the condition data can be based on expert rules, artificial intelligence (AI), or generative AI. Based on this analysis, assembly technicians initiate any necessary corrective actions to ensure optimal elevator operation. After completing the ride sequence, the system instructs the elevator control system to sample and send the measurement data to the cloud. The system then analyzes the telemetry data in near real-time and uses this data to generate a comprehensive commissioning report. Attached Figure Description

[0058] For the example below, see the attached diagram. Figure 1 The hardware scheme of the method steps according to the present invention is shown; and Figure 2 A flowchart of the process steps according to the present invention is shown.

[0059] Figure label: Door system 10 Motor 12 Encoder 13 Door operation control 14 Sensor 15 Car control panel 16 Elevator controller 18 Computer device 20 Network Computing Cloud 22 Acquisition Unit 30 Diagnostic Unit 32 Start running sequence 40 Acquire measurement data 42 Transmit measurement data 44 Data Preprocessing 46 Published analysis report 48 Detailed Implementation

[0060] This example illustrates the installation of automatic elevator shaft doors or components thereof, as these doors will be installed on the floor at the elevator shaft opening.

[0061] The installed door system 10 includes, in particular, a motor 12 for moving the door leaf, an encoder 13 (which is an example of all components related to the installation of the door system), and other components such as rolling bearing devices that support the door leaf, lighting systems connected to the door system, or indicator panels mounted on the floor. For the rolling bearings, vibration sensors capable of collecting data reflecting the functional quality of the rolling bearings can be installed.

[0062] Both motor 12 and encoder 13 are connected to door operation control 14, which is a subsystem of the elevator system. Another subsystem is car control panel 16, which is connected to door operation control 14 and serves as an intermediate system to elevator controller 18, the main elevator control system. These subsystems are interconnected via serial communication lines (e.g., CAN-BUS). Commands can be sent via the elevator controller 18 to open and / or close the doors, respectively, through the serial communication lines to the subsystems.

[0063] Motor 12 and encoder 13 also provide raw data. For example, the motor current required for a specific action can be measured as digital data by door operation controller 14. This data is used to assess the actual friction involved in closing and / or opening actions. Additionally, encoder 13, which counts the rotations of the motor, provides further raw data for assessing this friction. Supplementary raw data can also be collected from sensor 15, which, for example, detects vibrations of the tracks or rolling bearings supporting one or more door panels. Thus, modern elevators can generate a wealth of data from the entire subsystem of the automatic door and / or its specific components (such as motors or rolling bearings) by which the door moves along the drive track, or the motor drives the door. For example, the average friction of door system 10 can be estimated from this data using numerical parameters in Newtons through intelligent calculations, allowing these operational margins and the condition of the door system to be measured on commissioning day “zero.”

[0064] First, the doors are manually connected to the shaft walls and landing door openings. This assembly process includes drilling, positioning, and alignment, which is typically done manually. As a result, misalignment can occur, leading to variations in door friction associated with a particular door system, since there are several on different landings. Once physically installed, each door component is electrically and / or electronically connected to the elevator's main control system or its subsystems to control the doors and exchange data. During this initial setup, the elevator is set to diagnostic mode rather than normal operating mode to ensure a safe environment for testing.

[0065] When the door installation is completed, taking into account that the hardware has already been installed and the corresponding components have been connected to the serial communication system, the method of the present invention is further carried out by instructions given by the technician responsible for the installation. The technician obtains specific access to the elevator system through a computer device 20, such as a laptop, iPad, smartphone, or other digital maintenance terminal (such as an Android mini console), which executes the method through a network cloud 22 with communication units linked together.

[0066] The handheld computer, serving as a digital maintenance terminal, can be connected to the elevator controller 18 via a wire, such as a USB connector. Optionally and preferably, the connection is wireless. It can be connected via a wireless local area network (WLAN) through a router with internet access; a cellular network (3G, 4G, 5G), i.e., using mobile data provided by a telecommunications operator; Bluetooth (for smaller data transfers or as a bridge to internet-enabled devices); a wireless network, a network of wirelessly linked devices, or LoRaWAN or similar IoT networks designed for low-power applications such as IoT devices. The choice depends on speed, range, and availability.

[0067] Before initiating the computer-implemented commissioning sequence using the application on device 20, the system performs an authorization check to verify that the technician has the necessary permissions to access and control the elevator system within the scope of that commissioning. This ensures that only qualified personnel can initiate the commissioning process. Upon successful authorization, the installer will be granted temporary access to control elevator components specifically designed for testing, with access limited to the scope required for any particular commissioning.

[0068] The computer-implemented portion of the debugging process is then initiated using commands from the debugging application, triggering a controlled sequence of operations for the door components to perform functional tests. The diagnostic sequence program can utilize special test conditions that differ from normal operating parameters. For example, the opening and closing cycles can be implemented at different speeds, or embedded in the elevator's arrival at a floor, allowing for the simulation of real-world usage scenarios under close monitoring. During this controlled operation, sensors and data acquisition systems transmit data, from which key parameters such as door friction, load variations, vibration, and noise are calculated. These metrics provide fundamental insights into component quality and operational margins. Based on the specific component, the system selects relevant performance metrics that vary to accommodate the debugging requirements of each door.

[0069] During this functional test, measurement data is sent, collected, and stored in the memory of the acquisition unit 30. This acquisition unit may be part of the car control panel 16 and / or the door operation controller 14 and / or the elevator controller 18.

[0070] Once these measurements have been fully allocated in the acquisition unit 30, they are transmitted to the diagnostic unit 32, where the data can be preprocessed. The diagnostic unit 32 may be part of the elevator controller 18 or located within the elevator controller 18.

[0071] In this example, a fundamental interaction exists between the diagnostic unit 32 and the network cloud 22 for monitoring and analyzing system performance. The diagnostic unit, typically located at the device site, handles edge processing tasks such as sampling raw sensor data, performing statistical calculations, and filtering noise. Meanwhile, the network cloud 22 provides robust computing resources for more advanced analytics, such as determining whether the system meets or fails certain performance criteria based on processed data.

[0072] In this standard configuration, diagnostic unit 32 handles the initial processing steps. It collects raw sensor data, such as current values ​​or encoder pulse signals. The unit then extracts relevant measurement parameters, such as force or position, and refines them into more meaningful condition data, such as friction, using a predetermined algorithm. Edge processing, aggregation, or averaging of these values ​​(e.g., average friction) is then performed. The refined data is then sent to network cloud 22, where advanced analytics are applied. The cloud evaluates the data to determine outcomes, such as whether the system is operating normally or has failed. For example, the cloud might report: “Failure! Layer 5, Side A: Excessive Friction.” Diagnostic unit 32 may further involve limits established through historical data or machine learning algorithms, representing the expected operating conditions for specific components. In some cases, AI algorithms may dynamically adjust these thresholds based on real-time data or past installation experience, refining acceptable limits to reflect actual operational requirements.

[0073] Where real-time remote monitoring is possible, debug data can be transmitted to a remote service unit within the network computing cloud 22, allowing for real-time monitoring, analysis, and logging. This capability enables a rapid response to any anomalies detected during installation. Cloud-based processing enables big data analytics (such as data collected from feedback from numerous debuggers and assemblers), further enhancing the approach by leveraging real-time results and long-term data, and facilitating future maintenance planning through remote processing of certain analytical tasks. The cloud-based system also improves diagnostic accuracy through advanced algorithms and historical data interpretation, providing precise insights into the condition of each component.

[0074] Following analysis, an electronic report is generated summarizing the debugging results. This report specifies whether the tested components meet performance standards and highlights any areas where corrective actions may be necessary, such as realigning doors to reduce friction. The debugging results are sent to the remote service unit and to the technician's handheld computer device 20.

[0075] For elevators with multiple components undergoing commissioning in their door systems, the system evaluates each component sequentially, ensuring that commissioning of a component only proceeds if the performance of the preceding component falls within acceptable parameters. This structured process guarantees comprehensive quality checks of the entire elevator system.

[0076] The final verification process also archives all commissioning data in the diagnostic unit 32, creating a history that captures the condition of each component from the start of installation. This data serves as proof of the commissioning process, verifies correct installation, and provides a baseline for future evaluations. Furthermore, this record supports warranty verification and maintenance planning, providing insights into the elevator's performance over time.

[0077] Therefore, the entire commissioning process can be managed by a computer program, which can reside in a local controller unit or as part of a cloud server. This software controls the commissioning steps and standardizes the evaluation process, enhancing quality consistency. By reducing the initial failure rate, providing installers with data-driven feedback, and recording the entire commissioning sequence, this approach addresses common challenges in elevator installation, and of course, subsequent maintenance, as discussed below. This data-driven commissioning not only reduces early failures and unnecessary maintenance visits but also sets a high standard for installation quality, improving the operational reliability of the elevator.

[0078] This process enables real-time reporting to remote units, supporting data-driven insights into installation quality and enhancing on-site installation efficiency by providing targeted feedback based on the collected data. Furthermore, commissioning results are stored on non-transient media, maintaining a permanent record of test sequences and results, which is valuable for future evaluation and maintenance planning. This approach leverages its automated evaluation capabilities and cloud integration to transform elevator component commissioning into a reliable, consistent, and high-quality process, setting a new standard for installation and early diagnostics.

[0079] Figure 2 The flowchart illustrates the processing steps. The described method outlines a detailed process for evaluating and commissioning automatic elevator doors as components of an elevator system, ensuring accurate functional and performance alignment immediately after installation. The process begins with a sequence of operations 40, triggered by a start command from a handheld device such as a laptop, iPad, or iPhone, or other digital maintenance tools such as an Android mini console, after authorization for a technician. This involves activating the component under controlled conditions that may differ from normal operating modes. These conditions allow for specialized test sequences that provide enhanced insight into component functionality compared to standard operating tests. For example, door opening and closing can be tested in conjunction with the car reaching a specific floor, embedding door operation into the functionality of a larger system.

[0080] Data acquisition 42 occurs as the component runs through this sequence. The system collects measurement data, such as friction, vibration, and load variations, which are key performance indicators. These indicators are selected based on the component's commissioning requirements, guided by program instructions.

[0081] Next, the process involves transmitting data 44 to the diagnostic unit 32, where the collected data, calculated parameters, and performance limitations are analyzed. Data preprocessing 46 then begins. This includes establishing performance ranges.

[0082] The diagnostic unit then communicates with the network cloud 22, where the system uses pre-loaded data tables, machine learning algorithms, or AI-enabled calculations to set baseline values ​​for the data. These baselines define acceptable operating conditions and help identify potential problems such as misalignment or component wear. The analysis determines whether the component's performance meets expected thresholds.

[0083] Therefore, Network Cloud 22 summarizes commissioning results and provides actionable insights reflecting advanced capabilities, including cloud-based processing and communication with remote monitoring service units, enabling real-time monitoring and detailed data analysis. These capabilities leverage AI and historical data to improve diagnostic accuracy and provide actionable insights for installation teams. Results can also be stored for long-term tracking to support future assessments of component functionality throughout their lifespan. The published reports highlight current and past issues, identify potential failures, and provide step-by-step instructions for corrective action. Recommendations for inspection tools, procedures, and components, where applicable, are also provided.

[0084] This approach offers numerous advantages, such as reducing early failures, minimizing post-installation calibration maintenance, and improving installation quality. By maintaining comprehensive documentation of all tests, verifiable proof of successful commissioning is provided, and warranty claims are ensured to be supported by robust documentation.

[0085] Another example is described below, which illustrates an implementation of the method of the present invention after the entire elevator system has been installed in this way.

[0086] The elevator controller has been installed with the latest software and is operational. Shaft setup is complete, allowing the elevator to operate at rated speed in normal mode and automatically stop at designated floors. Automatic tuning has been performed, including configuration of speed controller settings and calibration of the load weighting device. Furthermore, the drive has been fine-tuned to achieve the desired level of ride comfort. The elevator car is currently empty, capable of operating to all floors, and its automatic doors can be opened and closed as needed. The elevator is connected to a network computing cloud for data exchange and remote operation.

[0087] The commissioning process begins with the elevator being driven to the second-lowest floor it serves. A power cycle is performed, in which the system is powered off and then back on to clear any existing condition data. The assembly technician responsible for commissioning then opens and launches the application of this invention, identifies the elevator under consideration, navigates to the equipment label, and selects the “Remote Commissioning Sequence” option from the remote operation menu. The system verifies that the commissioning sequence is available for the elevator. Once confirmed, the system initiates an elevator ride sequence to collect data.

[0088] First, the elevator travels to the lowest floor it serves and operates the automatic doors. Then, the elevator travels upwards to all the floors it serves, operating the automatic doors at each stop to generate door-related data. This travel process and subsequent door operations are repeated multiple times, for example, five cycles, to ensure the statistical confidence of the collected data. Next, the elevator makes multiple trips between the terminal floors, again running approximately five cycles, to generate non-door-related data, including metrics such as average elevator shaft friction and electrical balance. Throughout these sequences, telemetry information (including car status, door and fault events, and remote command responses) is continuously transmitted to the cloud.

[0089] After completing the ride sequence, the system instructs the elevator to sample and send the measurement data to the cloud. The system then analyzes the telemetry data in near real-time, using it to generate a comprehensive commissioning report. Assembly technicians access the report via a cloud connection interface to review the data and evaluate system performance. Based on this analysis, the technicians initiate any necessary corrective actions to ensure optimal elevator operation. This commissioning sequence can be adjusted for different settings and can have several variations; for example, certain steps in the sequence may be added or removed based on the measured condition data. The algorithm for analyzing the condition data can be based on expert rules, artificial intelligence (AI), or generative AI.

[0090] The benefits that this solution will bring: Measuring installation quality and reacting to it in the early stages of the commissioning sequence is quick and automated. Installers can initiate the commissioning sequence, wait for and check the results to see if any corrections are needed. If necessary, the commissioning sequence can be repeated to pass all checks and approve the installation. The results are mapped to the installation teams, and multiple teams are rated accordingly, so that the best-performing team can be selected for the installation when needed. Problems are minimized during the subsequent maintenance phase because the operating margin is in line with the design, as the installation quality has been measured and calibrated.

Claims

1. A method for commissioning a passenger conveyor system after installing at least one component, the system comprising: Conveyor; Controller (18), which is configured to operate the passenger conveyor; Data acquisition unit (30); And a diagnostic unit (32), which is communicatively coupled to and configured to monitor the at least one installed component, the method comprising semi-automatically or fully automatically performing the following computer-implemented steps for auditing the operation of the at least one installed component by means of a computing unit before putting the component into normal use: (40) A sequence of operations for said component is initiated by controlling said at least one installed component to perform at least one of its functions; The acquisition unit (30) acquires (42) receivable measurement data relating to the condition and / or operation of at least one component of the installed passenger conveyor system during the operating sequence; The measurement data is transmitted to the diagnostic unit (32) (44). The diagnostic unit (32) processes the data (46) by setting one or more parameter values ​​and setting corresponding limit values ​​for each parameter value, thereby defining the desired performance range for the at least one component. The diagnostic unit (32) determines the extent to which the acquired measurement data extends the defined performance range beyond the one or more limit values; Issue (48) an analytical report that determines the results.

2. The method according to claim 1, wherein, The passenger conveyor system is an elevator system, or an escalator system, or an escalator walkway, or a combination thereof.

3. The method according to claim 1 or 2, wherein, The method is performed after the installation of several components, wherein the control system is configured to execute each debugging method for each component in a sequential order, wherein the process steps proceed to the next component only if the previous component exhibits a limit value within its desired performance range.

4. The method according to claim 1 or 3, wherein, The method further includes the step of converting the determination result into at least one performance indicator, the at least one performance indicator being a qualitative value parameter and included in an electronic report.

5. The method according to any one of claims 1 to 4, wherein, The method further includes the step of transmitting the determination result to a remote service unit that is communicatively connected to the system.

6. The method according to any one of claims 1 to 5, wherein, The control system is connected to a computer network cloud system (22), and at least one step of the automation method is outsourced to the cloud, which communicates with the controller (18), for processing.

7. The method according to claim 6, wherein, The method is initiated and / or controlled online by the computing unit of the cloud system.

8. The method according to any one of claims 1 to 7, wherein, The analysis of the acquired measurement data is supported by artificial intelligence (AI) algorithms, which use trained machine learning units to assist in calculating the determined results and / or to assist in setting the one or more limit values.

9. A computer program residing on a computer-readable medium and comprising a set of instructions arranged to cause a computer or a group of computers to perform the computer-implemented method steps according to any one of claims 1 to 8.

10. A non-transient computer-readable medium storing a computer program according to claim 8, for performing computer-implemented steps for auditing the operation of the at least one installed component by means of a computing unit.

11. A method for installing components in a passenger conveyor system, wherein, The conveyor system includes at least a diagnostic unit (32) and a controller (18) configured to operate the conveyor system, wherein the method includes: Install the component in its intended location; The component is coupled to the controller (18) and the diagnostic unit (32) of the system; The component is debugged according to any one of claims 1 to 8; If the debugging results show that the condition and / or operation of the component is not within the expected performance range, then correct the installation and repeat the debugging method again; The debugging results are forwarded to the remote service unit.

12. The method according to claim 11, wherein, The method includes continuously forwarding the debugging results to the remote service unit.

13. A passenger conveyor system comprising: Multiple components; Controller (18), configured to operate the conveyor system; An acquisition unit (30) is configured to collect measurement data relating to the condition and / or operation of one or more of the plurality of components; a diagnostic unit (32) is communicatively coupled to the acquisition unit (30), wherein the diagnostic unit (32) includes a calculation unit configured to evaluate the performance of the at least one component by determining the extent to which the acquired measurement data exceeds a performance range defined by a limit value, and wherein the elevator system is configured to implement the method according to any one of claims 1 to 12.

14. The system according to claim 13, wherein, The diagnostic unit (32) is also configured to provide an electronic report indicating the determination of the extent to which the acquired measurement data exceeds the defined performance range and / or at least one performance index as a qualitative parameter of the extent.

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