Assisted transport system maintenance

Abstract

The name of the invention of the disclosure is' Assisted Transport System Maintenance '. A method of providing auxiliary transportation system maintenance includes receiving, at a prediction system, a request to analyze a change in an operating parameter of a transportation system; predicting, at a prediction system, an effect of changing an operating parameter of the transportation system; and sending, from the prediction system to the user device, a response including an effect of changing the operating parameter of the transportation system.

Description

Technical Field

[0001] Embodiments disclosed herein relate generally to transportation system maintenance, and more particularly to auxiliary transportation system maintenance. Background Art

[0002] Transportation system maintenance, for example, on an elevator system can be performed using a service tool or controller interface connected to an elevator controller for routine maintenance activities. Through the service tool or controller interface, maintenance personnel can view the operating parameters of the elevator system and adjust (one or more) operating parameters of the elevator system. Understanding the various operating parameters of an elevator system and their interdependencies can be difficult. Summary of the invention

[0003] According to an embodiment, a method for providing auxiliary transportation system maintenance includes: receiving a request to analyze changes in operating parameters of a transportation system at a prediction system; predicting, at the prediction system, the effects of changing the operating parameters of the transportation system; and sending a response including the effects of changing the operating parameters of the transportation system from the prediction system to a user device.

[0004] In addition to or as an alternative to one or more of the features described herein, further embodiments may include using one or more of the following to train the prediction system: engineering specifications, service records, customer complaints, and operating parameter change history.

[0005] In addition or alternatively to one or more of the features described herein, further embodiments may include wherein the response includes an impact analysis of changing the operating parameter of the transportation system.

[0006] In addition or as an alternative to one or more of the features described herein, further embodiments may include a dependency analysis wherein the response includes changing the operating parameter of the transport system.

[0007] In addition or alternatively to one or more of the features described herein, further embodiments may include wherein the response includes a visual indication of whether a change to the operating parameter of the transport system is recommended.

[0008] In addition to or as an alternative to one or more of the features described herein, further embodiments may include the forecasting system performing adaptive learning over time based on data points obtained from the transportation system.

[0009] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the transportation system is one of an elevator, an escalator, and a moving walkway.

[0010] According to another embodiment, a system for providing auxiliary transportation system maintenance includes: a prediction system that communicates with a user device via a network, the prediction system being configured to perform operations, the operations including: receiving a request at the prediction system to analyze changes in operating parameters of a transportation system; predicting, at the prediction system, an effect of changing the operating parameters of the transportation system; and sending a response from the prediction system to the user device including the effect of changing the operating parameters of the transportation system.

[0011] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the prediction system further performs: training the prediction system using one or more of the following: engineering specifications, service records, customer complaints, and operating parameter change history.

[0012] In addition or alternatively to one or more of the features described herein, further embodiments may include wherein the response includes an impact analysis of changing the operating parameter of the transportation system.

[0013] In addition or as an alternative to one or more of the features described herein, further embodiments may include a dependency analysis wherein the response includes changing the operating parameter of the transport system.

[0014] In addition or alternatively to one or more of the features described herein, further embodiments may include wherein the response includes a visual indication of whether a change to the operating parameter of the transport system is recommended.

[0015] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the forecasting system further performs: the forecasting system performs adaptive learning based on data points obtained from the transportation system over time.

[0016] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the transportation system is one of an elevator, an escalator, and a moving walkway.

[0017] According to another embodiment, a computer program product is embodied on a non-transitory computer-readable storage medium, the computer program product including instructions for causing a processor to implement a process for providing auxiliary transportation system maintenance, the process including: receiving a request at a prediction system to analyze changes in operating parameters of a transportation system; predicting, at the prediction system, an effect of changing the operating parameters of the transportation system; and sending a response from the prediction system to a user device including the effect of changing the operating parameters of the transportation system.

[0018] In addition to or as an alternative to one or more of the features described herein, further embodiments may include wherein the process further includes training the predictive system using one or more of the following: engineering specifications, service records, customer complaints, and operating parameter change history.

[0019] In addition or as an alternative to one or more of the features described herein, further embodiments may include wherein the response includes an impact analysis of changing the operating parameter of the transportation system.

[0020] In addition or as an alternative to one or more of the features described herein, further embodiments may include a dependency analysis wherein the response includes changing the operating parameter of the transport system.

[0021] In addition or alternatively to one or more of the features described herein, further embodiments may include wherein the response includes a visual indication of whether a change to the operating parameter of the transport system is recommended.

[0022] In addition or alternatively to one or more of the features described herein, further embodiments may include wherein the process further includes the forecasting system performing adaptive learning over time based on data points obtained from the transportation system.

[0023] Unless otherwise explicitly indicated, the aforementioned features and elements may be combined in various combinations without exclusivity. These features and elements and their operation will become more apparent from the following description and accompanying drawings. However, it should be understood that the following description and accompanying drawings are intended to be illustrative and explanatory in nature, rather than restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present disclosure is illustrated by way of example, and not limitation, in the accompanying figures, in which like references indicate similar elements.

[0025] Figure 1 is a schematic illustration of a transportation system in which various embodiments of the present disclosure may be employed.

[0026] Figure 2 A system for providing assisted transportation system maintenance in an example embodiment is depicted.

[0027] Figure 3 A method of providing auxiliary transportation system maintenance in an example embodiment is depicted.

[0028] Figure 4 Depicted are example interactions between a user device and a prediction system in an example embodiment.

[0029] Figure 5 A computing system in an example embodiment is shown. DETAILED DESCRIPTION

[0030] Figure 1 1 is a perspective view of a transport system 101 that includes an elevator car 103, a counterweight 105, a tension member 107, a guide rail 109, a machine 111, a position reference system 113, and a controller 115. The elevator car 103 and the counterweight 105 are interconnected by the tension member 107. The tension member 107 may include or be configured as, for example, a rope, a steel cable, and / or a coated steel belt. The counterweight 105 is configured to balance the load of the elevator car 103 and is configured to facilitate movement of the elevator car 103 within an elevator shaft or hoistway 117 and along the guide rail 109 relative to the counterweight 105 simultaneously and in opposite directions.

[0031] Tension member 107 engages machine 111, which is part of the overhead structure of transport system 101. Machine 111 is configured to control movement between elevator car 103 and counterweight 105. Position reference system 113 can be mounted on a fixed portion of the top of elevator shaft 117, such as on a support or guide rail, and can be configured to provide a position signal related to the position of elevator car 103 within elevator shaft 117. In other embodiments, position reference system 113 can be mounted directly to the moving components of machine 111, or can be located in other locations and / or configurations known in the art. Position reference system 113 can be any device or mechanism for monitoring the position of an elevator car and / or counterweight as known in the art. For example, but not limited to, position reference system 113 can be an encoder, sensor, or other system, and can include speed sensing, absolute position sensing, etc., as will be appreciated by those skilled in the art.

[0032] As shown, the controller 115 can be located in a controller room 121 of the elevator shaft 117 and is configured to control the operation of the transportation system 101, and in particular the operation of the elevator car 103. It is to be appreciated that the controller 115 need not be in the controller room 121, but can be in the hoistway or other locations in the transportation system 101. For example, the controller 115 can provide drive signals to the machine 111 to control the acceleration, deceleration, leveling, stopping, etc. of the elevator car 103. The controller 115 can also be configured to receive position signals from the position reference system 113 or any other desired position reference device. When moving up or down in the elevator shaft 117 along the guide rails 109, the elevator car 103 can stop at one or more landings 125, as controlled by the controller 115. Although shown in the controller room 121, it will be appreciated by those skilled in the art that the controller 115 can be located and / or configured at other locations or positions within the transportation system 101. In one embodiment, the controller 115 may be remotely located or located in a distributed computing network (eg, a cloud computing architecture). The controller 115 may be implemented using a processor-based machine, such as a personal computer, a server, a distributed computing network, or the like.

[0033] The machine 111 may include a motor or similar drive mechanism. According to an embodiment of the present disclosure, the machine 111 is configured to include an electric drive motor. The power source for the motor may be any power source, including an electrical grid, which, in combination with other components, is supplied to the motor. The machine 111 may include a traction sheave that applies force to the tension member 107 to move the elevator car 103 within the elevator shaft 117.

[0034] The transportation system 101 also includes one or more elevator doors 104. The elevator doors 104 can be attached to the elevator car 103, or the elevator doors 104 can be located on a landing 125 of the elevator system 101, or both. The embodiments disclosed herein can be applied to elevator doors 104 attached to the elevator car 103 or elevator doors 104 located on a landing 125 of the elevator system 101, or both. The elevator doors 104 open to allow passengers to enter and exit the elevator car 103.

[0035] Although shown and described with a roping system including tension members 107, elevator systems that employ other methods and mechanisms for moving an elevator car within an elevator shaft may employ embodiments of the present disclosure. For example, embodiments may be used in a ropeless elevator system that uses a linear motor to impart motion to an elevator car. Embodiments may also be used in a ropeless transmission system that uses a hydraulic lift to impart motion to an elevator car. Embodiments may also be used in a ropeless transmission system that uses a self-propelled elevator car (e.g., an elevator car equipped with friction wheels, pinch wheels, or traction wheels). Figure 1These are non-limiting examples presented for purposes of illustration and explanation only.

[0036] In other embodiments, the transportation system includes a transportation system that moves passengers between floors and / or along a single floor. Such transportation systems may include escalators, people movers, moving walkways, etc. Therefore, the embodiments described herein are not limited to systems such as Figure 1 An elevator transportation system such as the elevator transportation system shown.

[0037] Figure 2 A system 200 for providing auxiliary transportation system maintenance in an example embodiment is depicted. The system includes a user device 202 to be operated by a user, such as a maintenance person. The user device 202 may be a mobile smart device, such as a phone, a tablet, a laptop, etc., having a processor, a memory, a display, etc. The user device 202 may also be implemented using a fixed device, such as a personal computer, a server, a remote terminal, etc.

[0038] The user device 202 may be used during maintenance of the transportation system 101. During maintenance, the user device 202 communicates with the controller 115. The user device 202 may communicate with the controller 115 via a wired and / or wireless connection (e.g., WiFi, Bluetooth, NFC, LAN, CAN bus, etc.). The user device 202 may access maintenance records, faults, errors, alarms, etc. stored on the controller 115.

[0039] The user device 202 also communicates with the prediction system 214 via a network 210. The network 210 may be implemented via one or more networks, such as, but not limited to, one or more of: WiMax, a local area network (LAN), a wireless local area network (WLAN), a personal area network (PAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), a wireless wide area network (WWAN), or any broadband network, and may also be enabled using technologies such as, for example, Global System for Mobile Communications (GSM), Personal Communications Service (PCS), Bluetooth, WiFi, fixed wireless data, 2G, 2.5G, 3G (e.g., 3G networks based on WCDMA / UMTS), 4G, IMT-Advanced, pre-4G, 5G, LTE Advanced, mobile WiMax, WiMax 2. WirelessMAN-Advanced networks, Enhanced Data rates for GSM Evolution (EDGE), General Packet Radio Service (GPRS), Enhanced GPRS, iBurst, UMTS, HSPDA, HSUPA, HSPA, HSPA+, UMTS-TDD, 1xRTT, EV-DO, messaging protocols such as TCP / IP, SMS, MMS, Extensible Messaging and Presence Protocol (XMPP), Real-Time Messaging Protocol (RTMP), Instant Messaging and Presence Protocol (IMPP), Instant Messaging, USSD, IRC, or any other wireless data network, broadband network, or messaging protocol.

[0040] In some embodiments, user device 202 executes an application supported by the prediction system. In these embodiments, the user can connect to the prediction system 214 using the application installed on the user device. In other embodiments, a general interface such as a web browser is used to access the prediction system 214.

[0041] The prediction system 214 may be embodied as any type of processor-based computing or computer device capable of performing the functions described herein, including but not limited to computers, servers, workstations, desktop computers, laptop computers, notebook computers, tablet computers, mobile computing devices, wearable computing devices, network appliances, web appliances, distributed computing systems (e.g., cloud computing), processor-based systems, and / or consumer electronic devices.

[0042] Prediction system 214 may be implemented using a model of transportation system 101. Prediction system 214 may be implemented using an artificial intelligence (AI) system such as a large language model (LLM).

[0043] The database 218 communicates with the prediction system 214 and the user device 202 via the network 210. The database 218 may be implemented using known storage devices such as random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD), etc. The database 218 may be implemented using various technologies including a memory device in the prediction system 214, a memory device remote from the prediction system 214, cloud storage, etc.

[0044] Figure 3 A method of providing auxiliary transportation system maintenance in an example embodiment is depicted. The process begins at 300, where the prediction system 214 is trained. The prediction system 214 can be trained using a knowledge base of the transportation system 101 including one or more of engineering specifications, service records, customer complaints, operating parameter change history, etc. The prediction system 214 can also collect various data points from the controller 115, including data from elevator sensors, including but not limited to speed, temperature, door movement, load capacity, and energy consumption. In addition, data from usage patterns, such as peak usage times and maintenance history, can also be considered. The training can be performed using a machine learning algorithm to recognize patterns.

[0045] The machine learning training process may include data preprocessing, which converts the raw data form into a digital form suitable for the machine learning process. This may include one or more of the following: tokenization of natural language text derived from engineering specifications, service records, customer complaints; current and past controller parameter sets (one or more) as digital vectors; operating conditions (data collected from sensors) into digital vectors, and usage patterns and maintenance history as digital vectors.

[0046] The machine learning training process can include data training and validation data preparation, which splits the entire available data set into two data sets (training, validation). The usual split ratio followed is anywhere from (60% training, 40% validation) to (85% training, 15% validation). The validation data becomes the data points that the model will never see during training. The training data is the data on which the model is continuously trained.

[0047] The machine learning training process may include a training process. For machine learning models such as linear regression, logistic regression, decision trees, support vector machines, Xg-boost, etc., the training process may include (i) eliminating unimportant features (or data points) from the overall features (all numeric vectors). This is typically done using statistical methods such as correlation functions, information gain, regularization, etc.; and (ii) training based on selected important features. For neural network model (also known as deep learning) architectures, feature elimination is typically not required. Training of these models may include identifying different layers and layer sizes and defining an objective function based on purpose.

[0048] Embodiments of machine learning models can provide positive or negative impacts based on selected parameters. This can be implemented using binary classification models such as logistic regression, decision trees, neural networks, etc. Embodiments of machine learning models can provide impact analysis interpretation text. The text is generated using a model (or the same model as above) that can generate text by analysis. These models follow different architectures for knowledge-aware systems, such as LLM models. Some practical examples are Open AI ChatGPT, Bing Chat, Meta LLAMA2, Google Bard. These models typically follow a transformer architecture to generate text output based on the information provided as input.

[0049] Various models such as neural networks, decision trees, or support vector machines may be utilized to optimize accuracy and efficiency. The prediction system 214 may utilize a multi-language model to support multiple spoken languages, sign languages, and the like.

[0050] Prediction system 214 may be trained using data specific to the type of transportation system 101. For example, prediction system 214 may be trained based on the type of machine 111, whether elevator system 101 uses ropes or ropeless, number of floors, models of components (e.g., drive 111, controller 115, etc.), type of installation, etc. In this manner, prediction system 214 is trained using data relevant to a particular transportation system 101.

[0051] At 302, prediction system 214 receives a request from user device 202 to analyze a change in an operating parameter of transportation system 101. As part of a maintenance operation, a user may want to change an operating parameter of transportation system 101, but the user lacks sufficient knowledge or experience to fully understand how changing the operating parameter will affect the entire transportation system 101, or it may be difficult to predict without more advanced analysis.

[0052] The operating parameters may include various controllable parameters of the elevator system 101, such as, but not limited to, elevator car speed limit, elevator car acceleration limit, door dwell time, lighting intensity, car load limit, etc. Other example operating parameters include door reversal, door time closing protection, door time monitoring, double door operation, earthquake automatic recovery, emergency hospital service operation, and emergency power operation. Each of these operating parameters can be controlled by a subset of the related parameters of the feature. For example, the operating parameter door reopening time is a subset of door reversal and controls the door dwell time in the case of door reopening. In another example, the operating parameter emergency power group delay time is a subset of emergency power operation and controls the delay time in the case of emergency power operation.

[0053] At 304, the forecasting system 214 forecasts the impact of changing the operating parameters of the transportation system 101. This is done by changing the operating parameters in the forecasting system 214 model and observing the results. The results can then be compared to product specifications or other standards to determine whether to recommend changing the operating parameters of the transportation system 101. The forecasting system 214 can perform an impact analysis of changing the operating parameters of the transportation system 101. The impact analysis refers to the change of the operating parameters as a whole. Figure 4 An example of the impact analysis described herein is shown in . The impact analysis may cover the impact of changing more than one operating parameter simultaneously.

[0054] The prediction system 214 may perform a dependency analysis to determine which other operating parameters will be affected by changing an operating parameter of the transportation system 101. Dependency analysis refers to the interdependencies between operating parameters. For example, operating parameters such as "car load percentage" and "average passenger weight" are interdependent. When one operating parameter is changed, other (one or more) operating parameters will experience indirect effects at the system level. Dependency analysis covers the dependencies between operating parameters as well as the impact on each operating parameter.

[0055] At 306, prediction system 214 sends a response to user device 202 including the impact of changing the operating parameter of transportation system 101. The response may include a visual indication of whether changing the operating parameter of transportation system 101 is recommended (e.g., green for approval and red for prohibition). The response may include how changing the operating parameter of transportation system 101 affects other operating parameters of transportation system 101 (e.g., an impact analysis and / or a dependency analysis). At this stage, the user may accept the parameter change and start a trial run of transportation system 101 using the updated parameters.

[0056] At 308, the prediction system 214 may continuously monitor the operation of the transportation system 101 to collect data points from the controller 115, including data from elevator sensors, including but not limited to speed, noise, vibration, temperature, door movement, load capacity, and energy consumption. In addition, data from elevator usage patterns, such as peak usage times and maintenance history, may be considered. This same information may be used for initial training of the prediction system 214.

[0057] At 310, the forecasting system 214 can implement adaptive learning based on the data points obtained at 308. In this way, the forecasting system 214 continuously learns from updated data and maintenance results, improving its accuracy and forecasts over time.

[0058] Figure 4 An example interaction between user device 202 and prediction system 214 in an example embodiment is depicted. In this example, a user requests prediction system 214, via user device 202, to consider the impact of increasing elevator door dwell time by 5 seconds. Prediction system 214 provides a response including whether the change is recommended and the impact that the change will have on other operating parameters (e.g., an impact analysis and / or a dependency analysis).

[0059] Now turn to Figure 5 , a computer system 600 is generally shown according to an embodiment. The computer system 600 can be used to implement the prediction system 214. As described herein, the computer system 600 can be an electronic computer framework that includes and / or employs any number of computing devices and networks and combinations thereof that utilize various communication technologies. The computer system 600 can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of other features. The computer system 600 can be, for example, a server, a desktop computer, a laptop computer, a tablet computer, or a smart phone. In some examples, the computer system 600 can be a cloud computing node. The computer system 600 can be described in the general context of computer system executable instructions such as program modules executed by the computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, etc. that perform specific tasks or implement specific abstract data types. The computer system 600 can be practiced in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located in both local and remote computer system storage media including memory storage devices.

[0060] like Figure 5As shown, the computer system 600 has one or more central processing units (CPU(s)) 601a, 601b, 601c, etc. (collectively or generally referred to as processor(s) 601). The processor 601 may be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. The processor 601 (also referred to as a processing circuit) is coupled to a system memory 603 and various other components via a system bus 602. The system memory 603 may include a read-only memory (ROM) 604 and a random access memory (RAM) 605. The ROM 604 is coupled to the system bus 602 and may include a basic input / output system (BIOS) that controls certain basic functions of the computer system 600. The RAM is a read-write memory coupled to the system bus 602 for use by the processor 601. The system memory 603 provides temporary storage space for the operation of the instructions during operation. The system memory 603 may include a random access memory (RAM), a read-only memory, a flash memory, or any other suitable memory system.

[0061] Computer system 600 includes an input / output (I / O) adapter 606 and a communication adapter 607 coupled to system bus 602. I / O adapter 606 may be a small computer system interface (SCSI) adapter that communicates with hard disk 608 and / or any other similar components. I / O adapter 606 and hard disk 608 are collectively referred to herein as mass storage device 610.

[0062] Software 611 for execution on computer system 600 may be stored in mass storage device 610. Mass storage device 610 is an example of a tangible storage medium readable by processor 601, wherein software 611 is stored as instructions for execution by processor 601 to cause computer system 600 to operate, such as described below with reference to the various figures. Examples of computer program products and execution of such instructions are discussed in greater detail herein. Communications adapter 607 interconnects system bus 602 with network 612, which may be an external network, enabling computer system 600 to communicate with other such systems. In one embodiment, a portion of system memory 603 and mass storage device 610 together store an operating system, which may be any suitable operating system, such as z / OS or AIX operating systems from IBM Corporation, to coordinate Figure 5 The functions of the various components are shown.

[0063] Additional input / output devices are shown connected to the system bus 602 via a display adapter 615 and an interface adapter 616. In one embodiment, adapters 606, 607, 615, and 616 may be connected to one or more I / O buses that are connected to the system bus 602 via an intermediate bus bridge (not shown). A display 619 (e.g., a screen or display monitor) is connected to the system bus 602 via a display adapter 615, which may include a graphics controller and a video controller to improve the performance of graphics-intensive applications. A keyboard 621, mouse 622, speaker 623, and the like may be interconnected to the system bus 602 via an interface adapter 616, which may include, for example, a super I / O chip that integrates multiple device adapters into a single integrated circuit. Suitable I / O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Thus, as Figure 5 As configured in, the computer system 600 includes processing capabilities in the form of a processor 601, as well as storage capabilities including system memory 603 and mass storage device 610, input components such as keyboard 621 and mouse 622, and output capabilities including speakers 623 and display 619.

[0064] In some embodiments, the communication adapter 607 can use any suitable interface or protocol (such as Internet Small Computer System Interface, etc.) to transmit data. The network 612 can be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, etc. An external computing device can be connected to the computer system 600 through the network 612. In some examples, the external computing device can be an external web server or a cloud computing node.

[0065] To understand, Figure 5 The block diagram is not intended to indicate that the computer system 600 is to include Figure 5 Rather, computer system 600 may include any suitable fewer or Figure 5 600 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). In addition, the embodiments described herein with respect to computer system 600 may be implemented with any appropriate logic, wherein in various embodiments, the logic referred to herein may include any appropriate hardware (e.g., a processor, an embedded controller, or an application-specific integrated circuit, etc.), software (e.g., an application, etc.), firmware, or any suitable combination of hardware, software, and firmware.

[0066] As described above, embodiments may take the form of processor-implemented processes and devices for practicing those processes, such as the processor in prediction system 214. Embodiments may also take the form of computer program code containing instructions embodied in a tangible medium, such as a network cloud storage, an SD card, a flash drive, a floppy disk, a CD ROM, a hard drive, or any other computer-readable storage medium, wherein when the computer program code is loaded into and executed by a computer, the computer becomes a device for practicing the embodiments. Embodiments may also take the form of computer program code, for example, whether stored in a storage medium, loaded into and / or executed by a computer, or transmitted through some transmission medium, such as by wire or cable, by optical fiber, or via electromagnetic radiation, wherein when the computer program code is loaded into and executed by a computer, the computer becomes a device for practicing the embodiments. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.

[0067] The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a, an" and "the" are intended to also include plural forms unless the context clearly indicates otherwise. It will also be understood that the terms "include" and / or "comprise" when used in this specification specify the presence of the features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, element components and / or groups thereof.

[0068] It will be appreciated by those skilled in the art that various example embodiments are shown and described herein, each of which has certain features in a particular embodiment, but the present disclosure is not limited thereto. On the contrary, the present disclosure may be modified to incorporate any number of variations, alterations, substitutions, combinations, sub-combinations, or equivalent arrangements not previously described but commensurate with the scope of the present disclosure. In addition, although various embodiments of the present disclosure have been described, it is to be understood that aspects of the present disclosure may include only some of the described embodiments. Therefore, the present disclosure should not be considered to be limited by the foregoing description, but only by the scope of the appended claims.

Claims

1. A method for providing auxiliary transportation system maintenance, the method comprising: receiving, at the forecasting system, a request to analyze a change in an operating parameter of the transportation system; at the prediction system, predicting an effect of changing the operating parameter of the transportation system; as well as A response including the effect of changing the operating parameter of the transportation system is sent from the prediction system to a user device.

2. The method according to claim 1, further comprising: The prediction system is trained using one or more of the following: engineering specifications, service records, customer complaints, and operating parameter change history.

3. The method according to claim 1, wherein: The response includes an analysis of the effects of changing the operating parameters of the transportation system.

4. The method according to claim 1, wherein: The response includes a dependency analysis of changing the operating parameter of the transportation system.

5. The method according to claim 1, wherein: The response includes a visual indication of whether a change in the operating parameter of the transport system is recommended.

6. The method according to claim 1, further comprising: The forecasting system performs adaptive learning over time based on data points obtained from the transportation system.

7. The method according to claim 1, wherein: The transportation system is one of an elevator, an escalator and a moving walkway.

8. A system for providing auxiliary transportation system maintenance, the system comprising: A prediction system in communication with a user device via a network, the prediction system being configured to perform operations, the operations comprising: receiving, at the forecasting system, a request to analyze a change in an operating parameter of a transportation system; At the prediction system, predicting the effects of changing the operating parameters of the transportation system; and A response including the effect of changing the operating parameter of the transportation system is sent from the prediction system to the user device.

9. The system according to claim 8, wherein: The prediction system further performs: training the prediction system using one or more of the following: engineering specifications, service records, customer complaints, and operating parameter change history.

10. The system according to claim 8, wherein: The response includes an analysis of the effects of changing the operating parameters of the transportation system.

11. The system according to claim 8, wherein: The response includes a dependency analysis of changing the operating parameter of the transportation system.

12. The system according to claim 8, wherein: The response includes a visual indication of whether a change in the operating parameter of the transport system is recommended.

13. The system according to claim 8, wherein: The forecasting system further performs: the forecasting system performs adaptive learning over time based on data points obtained from the transportation system.

14. The system according to claim 8, wherein: The transportation system is one of an elevator, an escalator and a moving walkway.

15. A computer program product embodied on a non-transitory computer readable storage medium, the computer program product comprising instructions for causing a processor to implement a process for providing paratransit system maintenance, the process comprising: receiving, at the forecasting system, a request to analyze a change in an operating parameter of the transportation system; at the prediction system, predicting an effect of changing the operating parameter of the transportation system; as well as A response including the effect of changing the operating parameter of the transportation system is sent from the prediction system to a user device.

16. The computer program product of claim 15, wherein: The process further comprises: The prediction system is trained using one or more of the following: engineering specifications, service records, customer complaints, and operating parameter change history.

17. The computer program product of claim 15, wherein: The response includes an analysis of the effects of changing the operating parameters of the transportation system.

18. The computer program product of claim 15, wherein: The response includes a dependency analysis of changing the operating parameter of the transportation system.

19. The computer program product of claim 15, wherein: The response includes a visual indication of whether a change in the operating parameter of the transport system is recommended.

20. The computer program product of claim 15, wherein: The process further comprises: The forecasting system performs adaptive learning over time based on data points obtained from the transportation system.

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