Patent Description:
A health of a conveyance apparatus within a conveyance systems, such as, for example, elevator systems, escalator systems, and moving walkways may be difficult and/or costly to determine.

<CIT> discloses a passenger conveyor diagnostic system including a mobile terminal in signal communication with a cloud computing network.

XP <NUM><NUM><NUM> A demonstrates how IoT technology can enable highly distributed elevator equipment servicing by using remote monitoring technology.

According to a first aspect of the invention, there is provided a monitoring system for monitoring an escalator as claimed in claim <NUM>.

Further embodiments may include that the sensing apparatus is configured to determine a condition based monitoring (CBM) health score in response to at least one of the acceleration data and the sound data.

Further embodiments may include that the sensing apparatus is configured to transmit the acceleration data and the sound data to the local gateway device and the local gateway device is configured to determine a CBM health score in response to at least one of the acceleration data and the sound data.

Further embodiments may include an analytic engine in communication with the local gateway device through a cloud computing network, wherein the sensing apparatus is configured to transmit the acceleration data and the sound data to the analytic engine through the local gateway device and the cloud computing network, and wherein the analytic engine is configured to determine a CBM health score in response to at least one of the acceleration data and the sound data.

Further embodiments may include that the sensing apparatus is attached to a moving component of a drive machine of the escalator.

Further embodiments may include that the moving component of the drive machine is an output sheave that drives a step chain of the escalator.

According to a further aspect of the invention, there is provided a method of monitoring an escalator as claimed in claim <NUM>.

Further embodiments may include that the CBM health score is determined in response to at least one of the acceleration data and the sound data.

Further embodiments may include that the sensing apparatus is configured to determine a CBM health score in response to at least one of the acceleration data and the sound data.

Further embodiments may include: transmitting the acceleration data and the sound data to a local gateway device in wireless communication with the sensing apparatus through a short-range wireless protocol, wherein the local gateway device is configured to determine a CBM health score in response to at least one of the acceleration data and the sound data.

Further embodiments may include: transmitting the acceleration data and the sound data to a local gateway device in wireless communication with the sensing apparatus through a short-range wireless protocol; and transmitting the acceleration data and the sound data to an analytic engine through a cloud computing network, wherein the analytic engine is configured to determine a CBM health score in response to at least one of the acceleration data and the sound data.

Further embodiments include determining an operating mode of the escalator in response to at least one of the acceleration data and the sound data.

Technical effects of embodiments of the present disclosure include monitoring operation of an escalator using at least on of sound and accelerations.

The foregoing features and elements may be combined in various combinations within the scope of the appended claims.

<FIG> illustrates an escalator <NUM>. It should become apparent in the ensuing description that the invention is applicable to other passenger conveyor systems, such as moving walks. The escalator <NUM> generally includes a truss <NUM> extending between a lower landing <NUM> and an upper landing <NUM>. A plurality of sequentially connected steps or tread plates <NUM> are connected to a step chain <NUM> and travel through a closed loop path within the truss <NUM>. A pair of balustrades <NUM> includes moving handrails <NUM>. A drive machine <NUM>, or drive system, is typically located in a machine space <NUM> under the upper landing <NUM>; however, an additional machine space <NUM>' can be located under the lower landing <NUM>. The drive machine <NUM> is configured to drive the tread plates <NUM> and/or handrails <NUM> through the step chain <NUM>. The drive machine <NUM> operates to move the tread plates <NUM> in a chosen direction at a desired speed under normal operating conditions.

The tread plates <NUM> make a <NUM> degree heading change in a turn-around area <NUM> located under the lower landing <NUM> and upper landing <NUM>. The tread plates <NUM> are pivotally attached to the step chain <NUM> and follow a closed loop path of the step chain <NUM>, running from one landing to the other, and back again.

The drive machine <NUM> includes a first drive member <NUM>, such as motor output sheave, connected to a drive motor <NUM> through a belt reduction assembly <NUM> including a second drive member <NUM>, such as an output sheave, driven by a tension member <NUM>, such as an output belt. The first drive member <NUM> in some embodiments is a driving member, and the second drive member <NUM> is a driven member.

As used herein, the first drive member <NUM> and/or the second drive member, in various embodiments, may be any type of rotational device, such as a sheave, pulley, gear, wheel, sprocket, cog, pinion, etc. The tension member <NUM>, in various embodiments, can be configured as a chain, belt, cable, ribbon, band, strip, or any other similar device that operatively connects two elements to provide a driving force from one element to another. For example, the tension member <NUM> may be any type of interconnecting member that extends between and operatively connects the first drive member <NUM> and a second drive member <NUM>. In some embodiments, as shown in <FIG>, the first drive member <NUM> and the second drive member may provide a belt reduction. For example, first drive member <NUM> may be approximately <NUM> (<NUM> inches) in diameter while the second drive member <NUM> may be approximately <NUM> (<NUM> inches) in diameter. The belt reduction, for example, allows the replacement of sheaves to change the speed for <NUM> or <NUM> electrical supply power applications, or different step speeds. However, in other embodiments the second drive member <NUM> may be substantially similar to the first drive member <NUM>.

As noted, the first drive member <NUM> is driven by drive motor <NUM> and thus is configured to drive the tension member <NUM> and the second drive member <NUM>. In some embodiments the second drive member <NUM> may be an idle gear or similar device that is driven by the operative connection between the first drive member <NUM> and the second drive member <NUM> by means of tension member <NUM>. The tension member <NUM> travels around a loop set by the first drive member <NUM> and the second drive member <NUM>, which herein after may be referred to as a small loop. The small loop is provided for driving a larger loop which consists of the step chain <NUM>, and is driven by an output sheave <NUM>, for example. Under normal operating conditions, the tension member <NUM> and the step chain <NUM> move in unison, based upon the speed of movement of the first drive member <NUM> as driven by the drive motor <NUM>.

The escalator <NUM> also includes a controller <NUM> that is in electronic communication with the drive motor <NUM>. The controller <NUM> may be located, as shown, in the machine space <NUM> of the escalator <NUM> and is configured to control the operation of the escalator <NUM>. For example, the controller <NUM> may provide drive signals to the drive motor <NUM> to control the acceleration, deceleration, stopping, etc. of the tread plates <NUM> through the step chain <NUM>. The controller <NUM> may be an electronic controller including a processor and an associated memory comprising computer-executable instructions that, when executed by the processor, cause the processor to perform various operations. The processor may be, but is not limited to, a single-processor or multi-processor system of any of a wide array of possible architectures, including field programmable gate array (FPGA), central processing unit (CPU), application specific integrated circuits (ASIC), digital signal processor (DSP) or graphics processing unit (GPU) hardware arranged homogenously or heterogeneously. The memory may be but is not limited to a random access memory (RAM), read only memory (ROM), or other electronic, optical, magnetic or any other computer readable medium.

Although described herein as a particular escalator drive system and particular components, this is merely exemplary, and those of skill in the art will appreciate that other escalator system configurations may operate with the invention disclosed herein.

The elements and components of escalator <NUM> may suffer from fatigue, wear and tear, or other damage such that diminish health of the escalator <NUM>. The embodiments disclosed herein seek to provide a health monitoring system <NUM> for the escalator <NUM> of <FIG>.

An escalator health monitoring system <NUM> is illustrated in <FIG>, according to an embodiment of the present disclosure. The escalator health monitoring system <NUM> includes one or more sensing apparatus <NUM> configured to detect sensor data <NUM> of the escalator <NUM>, process the sensor data <NUM>, and transmit the processed sensor data <NUM> (e.g., a condition based monitoring (CBM) health score <NUM>) to a cloud connected analytic engine <NUM>. Alternatively, the sensor data <NUM> may be sent raw to at least one of a local gateway device <NUM> and an analytic engine <NUM>, where the sensor data <NUM> will be processed.

Sensor data <NUM> may include but is not limited to pressure data <NUM>, vibratory signatures (i.e., vibrations over a period of time) or acceleration data <NUM>, and sound data <NUM>. The acceleration data <NUM> may derivatives or integrals of acceleration data <NUM> of the escalator <NUM>, such as, for example, location distance, velocity, jerk, jounce, snap. etc. Sensor data <NUM> may also include light, humidity, and temperature data, or any other desired data parameter. It should be appreciated that, although particular systems are separately defined in the schematic block diagrams, each or any of the systems may be otherwise combined or separated via hardware and/or software.

The escalator health monitoring system <NUM> may include one or more sensing apparatus <NUM> located in various locations of the escalator <NUM>. In one example, a sensing apparatus <NUM> is located attached to or within the handrails <NUM> and move with the handrails <NUM>. In another example, a sensing apparatus <NUM> is stationary and is located proximate the drive machine <NUM> or step chain <NUM>. In another example, a sensing apparatus <NUM> is attached to the step chain <NUM> and moving with the moving step chain <NUM>. In another example, a sensing apparatus <NUM> may be attached to the tread plate <NUM> and moving with the tread plate <NUM>. In another example, a sensing apparatus <NUM> may be attached to the drive machine <NUM> and moving relative to the moving step chain <NUM>. In another embodiment, the sensing apparatus <NUM> may be attached to a moving component of the drive machine <NUM>. The moving component of the drive machine <NUM> may be output sheave <NUM> that drives a step chain <NUM> of the escalator <NUM>.

In an embodiment, the sensing apparatus <NUM> is configured to process the sensor data <NUM> prior to transmitting the sensor data <NUM> to the analytic engine <NUM> through a processing method, such as, for example, edge processing. Advantageously, utilizing edge processing helps save energy by reducing the amount of data that needs to be transferred. In another embodiment, the sensing apparatus <NUM> is configured to transmit sensor data <NUM> that is raw and unprocessed to a analytic engine <NUM> for processing.

The processing of the sensor data <NUM> may reveal data, such as, for example, vibrations, vibratory signatures, sounds, temperature, acceleration of the escalator <NUM>, deceleration of the escalator, escalator ride performance, emergency stops, etc..

The analytic engine <NUM> may be a computing device, such as, for example, a desktop, a cloud based computer, and/or a cloud based artificial intelligence (AI) computing system. The analytic engine <NUM> may also be a computing device that is typically carried by a person, such as, for example a smartphone, PDA, smartwatch, tablet, laptop, etc. The analytic engine <NUM> may also be two separate devices that are synced together, such as, for example, a cellular phone and a desktop computer synced over an internet connection.

The analytic engine <NUM> may be an electronic controller including a processor <NUM> and an associated memory <NUM> comprising computer-executable instructions that, when executed by the processor <NUM>, cause the processor <NUM> to perform various operations. The processor <NUM> may be, but is not limited to, a single-processor or multi-processor system of any of a wide array of possible architectures, including field programmable gate array (FPGA), central processing unit (CPU), application specific integrated circuits (ASIC), digital signal processor (DSP) or graphics processing unit (GPU) hardware arranged homogenously or heterogeneously. The memory <NUM> may be but is not limited to a random access memory (RAM), read only memory (ROM), or other electronic, optical, magnetic or any other computer readable medium.

The sensing apparatus <NUM> is configured to transmit the sensor data <NUM> that is raw or processed to a local gateway device <NUM> via short-range wireless protocols <NUM>. Short-range wireless protocols <NUM> may include but are not limited to Bluetooth, BLE, Wi-Fi, LoRa, insignu, enOcean, Sigfox, HaLow (<NUM>. 11ah), zWave, ZigBee, Wireless M-Bus or other short-range wireless protocol known to one of skill in the art. In an embodiment, the local gateway device <NUM> may utilize message queuing telemetry transport (MQTT or MQTT SN) to communicate with the sensing apparatus <NUM>. Advantageously, MQTT minimizes network bandwidth and device resource requirements, which helps reduce power consumption amongst the local gateway device <NUM> and the sensing apparatus <NUM>, while helping to ensure reliability and message delivery. Using short-range wireless protocols <NUM>, the sensing apparatus <NUM> is configured to transmit the sensor data <NUM> that is raw or processed directly the local gateway device <NUM> and the local gateway device <NUM> is configured to transmit the sensor data <NUM> that is raw or processed to the analytic engine <NUM> through a network <NUM> or to the controller <NUM>. The network <NUM> may be a computing network, such as, for example, a cloud computing network, cellular network, or any other computing network known to one of skill in the art. Using long-range wireless protocols <NUM>, the sensing apparatus <NUM> is configured to transmit the sensor data <NUM> to the analytic engine <NUM> through a network <NUM>. Long-range wireless protocols <NUM> may include but are not limited to cellular, LTE (NB-IoT, CAT M1), LoRa, Satellite, Ingenu, or SigFox. The local gateway device <NUM> may be in communication with the controller <NUM> through a hardwired and/or wireless connection using short-range wireless protocols <NUM>.

The sensing apparatus <NUM> may be configured to detect sensor data <NUM> including acceleration in any number of directions. In an embodiment, the sensing apparatus <NUM> may detect sensor data <NUM> including acceleration data <NUM> along three axis, an X axis, a Y axis, and a Z axis. As illustrated in <FIG>, the X axis and Y axis may form a plane parallel to the tread plate <NUM> and the Z axis are perpendicular to the tread plate <NUM>. The Z axis is parallel to the vertical direction or direction of gravity. The X is parallel to the horizontal movement of the tread plates <NUM>, whereas the Y axis is perpendicular to the horizontal movement of the tread plates <NUM>.

Also shown in <FIG> is a computing device <NUM>. The computing device <NUM> may belong to an escalator mechanic/technician working on or monitoring the escalator <NUM>. The computing device <NUM> may be a computing device such as a desktop computer or a mobile computing device that is typically carried by a person, such as, for example a smart phone, PDA, smart watch, tablet, laptop, etc. The computing device <NUM> may include a display device <NUM> so that the mechanic may visually see a CBM health score <NUM> of the escalator <NUM> or sensor data <NUM>. The computing device <NUM> may include a processor <NUM>, memory <NUM>, a communication module <NUM>, and an application <NUM>, as shown in <FIG>. The processor <NUM> can be any type or combination of computer processors, such as a microprocessor, microcontroller, digital signal processor, application specific integrated circuit, programmable logic device, and/or field programmable gate array. The memory <NUM> is an example of a non-transitory computer readable storage medium tangibly embodied in the computing device <NUM> including executable instructions stored therein, for instance, as firmware. The communication module <NUM> may implement one or more communication protocols, such as, for example, short-range wireless protocols <NUM> and long-range wireless protocols <NUM>. The communication module <NUM> may be in communication with at least one of the controller <NUM>, the sensing apparatus <NUM>, the network <NUM>, and the analytic engine <NUM>. In an embodiment, the communication module <NUM> may be in communication with the analytic engine <NUM> through the network <NUM>.

The communication module <NUM> is configured to receive a CBM health score <NUM> and/or sensor data <NUM> from the network <NUM>, and the analytic engine <NUM>. The application <NUM> is configured to generate a graphical user interface on the computing device <NUM> to display the CBM health score <NUM>. The application <NUM> may be computer software installed directly on the memory <NUM> of the computing device <NUM> and/or installed remotely and accessible through the computing device <NUM> (e.g., software as a service).

<FIG> illustrates a block diagram of the sensing apparatus <NUM> of the escalator health monitoring system <NUM> of <FIG>. It should be appreciated that, although particular systems are separately defined in the schematic block diagram of <FIG>, each or any of the systems may be otherwise combined or separated via hardware and/or software. As shown in <FIG>, the sensing apparatus <NUM> may include a controller <NUM>, a plurality of sensors <NUM> in communication with the controller <NUM>, a communication module <NUM> in communication with the controller <NUM>, and a power source <NUM> electrically connected to the controller <NUM>.

The plurality of sensors <NUM> includes an inertial measurement unit (IMU) sensor <NUM> configured to detect sensor data <NUM> including acceleration data <NUM> of the sensing apparatus <NUM> and the escalator <NUM>. The IMU sensor <NUM> may be a sensor, such as, for example, an accelerometer, a gyroscope, or a similar sensor known to one of skill in the art. The acceleration data <NUM> detected by the IMU sensor <NUM> may include accelerations as well as derivatives or integrals of accelerations, such as, for example, velocity, jerk, jounce, snap. etc. The IMU sensor <NUM> is in communication with the controller <NUM> of the sensing apparatus <NUM>.

The plurality of sensors <NUM> includes a pressure sensor <NUM> configured to detect sensor data <NUM> including pressure data <NUM>, such as, for example, atmospheric air pressure proximate the escalator <NUM>. The pressure sensor <NUM> may be a pressure altimeter or barometric altimeter in two non-limiting examples. The pressure sensor <NUM> is in communication with the controller <NUM>.

The plurality of sensors <NUM> includes a microphone <NUM> configured to detect sensor data <NUM> including sound data <NUM>, such as, for example audible sound and sound levels. The microphone <NUM> may be a 2D (e.g., stereo) or 3D microphone. The microphone <NUM> is in communication with the controller <NUM>.

The plurality of sensors <NUM> may also include additional sensors including but not limited to a light sensor <NUM>, a pressure sensor <NUM>, a humidity sensor <NUM>, and a temperature sensor <NUM>. The light sensor <NUM> is configured to detect sensor data <NUM> including light exposure. The light sensor <NUM> is in communication with the controller <NUM>. The humidity sensor <NUM> is configured to detect sensor data <NUM> including humidity levels. The humidity sensor <NUM> is in communication with the controller <NUM>. The temperature sensor <NUM> is configured to detect sensor data <NUM> including temperature levels. The temperature sensor <NUM> is in communication with the controller <NUM>.

The plurality of sensors <NUM> of the sensing apparatus <NUM> may be utilized to determine various operating modes of the escalator <NUM>. Any one of the plurality of sensors <NUM> may be utilized to determine that the escalator <NUM> is running. For example, the microphone <NUM> may detect a characteristic noise indicating that the escalator <NUM> is running or the IMU sensor <NUM> may detect a characteristic acceleration indicating that the escalator <NUM> is running. The pressure sensor <NUM> may be utilized to determine a running speed of the escalator <NUM>. For example, if the sensing apparatus <NUM> is located on the step chain <NUM> or the tread plate <NUM>, a continuous or constant air pressure change may indicate movement of the step chain <NUM> and thus the running speed may be determined in response to the change in air pressure. The IMU sensor <NUM> may be utilized to determine a height of the escalator <NUM>. For example, if the sensing apparatus <NUM> is located on the handrail <NUM> or the tread plate <NUM>, a change in direction of velocity (e.g., step is moving up and then suddenly moving down) may indicate that the handrail <NUM> or tread plate <NUM> has reached a maximum height. The IMU sensor <NUM> may be utilized to determine a braking distance of the escalator <NUM>. For example, if the sensing apparatus <NUM> is located on the handrail <NUM>, the step chain <NUM>, or the tread plate <NUM>, the second integral of deceleration of the sensing apparatus <NUM> may be calculated to determine braking distance. Braking distance may be determined from acceleration data <NUM> indicating an acceleration above threshold to a first zero-crossing of filtered sensor data (integrated speed from measured vibration of the acceleration data <NUM>). The IMU sensor <NUM> may be utilized to determine an occupancy state of the escalator <NUM>. For example, if the sensing apparatus <NUM> is located on the step chain <NUM> or the tread plate <NUM>, vibrations detected by the sensing apparatus <NUM> using the IMU sensor <NUM> may indicate entry of passengers onto the escalator <NUM> or exit of passengers off the escalator <NUM>.

The controller <NUM> of the sensing apparatus <NUM> includes a processor <NUM> and an associated memory <NUM> comprising computer-executable instructions that, when executed by the processor <NUM>, cause the processor <NUM> to perform various operations, such as, for example, edge pre-processing or processing the sensor data <NUM> collected by the IMU sensor <NUM>, the light sensor <NUM>, the pressure sensor <NUM>, the microphone <NUM>, the humidity sensor <NUM>, and the temperature sensor <NUM>. In an embodiment, the controller <NUM> may process the acceleration data <NUM> and/or the pressure data <NUM> in order to determine an elevation of the sensing apparatus <NUM> if the sensing apparatus <NUM> is on a component that rises or falls during operation of the escalator <NUM>, such as, for example, on the handrail <NUM> and step chain <NUM>. In an embodiment the controller <NUM> of the sensing apparatus <NUM> may utilize a Fast Fourier Transform (FFT) algorithm to process the sensor data <NUM>.

The processor <NUM> may be but is not limited to a single-processor or multi-processor system of any of a wide array of possible architectures, including field programmable gate array (FPGA), central processing unit (CPU), application specific integrated circuits (ASIC), digital signal processor (DSP) or graphics processing unit (GPU) hardware arranged homogenously or heterogeneously. The memory <NUM> may be a storage device, such as, for example, a random access memory (RAM), read only memory (ROM), or other electronic, optical, magnetic or any other computer readable medium.

The power source <NUM> of the sensing apparatus <NUM> is configured to store and/or supply electrical power to the sensing apparatus <NUM>. The power source <NUM> may include an energy storage system, such as, for example, a battery system, capacitor, or other energy storage system known to one of skill in the art. The power source <NUM> may also generate electrical power for the sensing apparatus <NUM>. The power source <NUM> may also include an energy generation or electricity harvesting system, such as, for example synchronous generator, induction generator, or other type of electrical generator known to one of skill in the art. The power source <NUM> may also be a hardwired power supply that is hardwired to and receives electricity from an electrical grid and/or the escalator <NUM>.

The sensing apparatus <NUM> includes a communication module <NUM> configured to allow the controller <NUM> of the sensing apparatus <NUM> to communicate with the local gateway device <NUM> through short-range wireless protocols <NUM>. The communication module <NUM> may be configured to communicate with the local gateway device <NUM> using short-range wireless protocols <NUM>, such as, for example, Bluetooth, BLE, Wi-Fi, LoRa, insignu, enOcean, Sigfox, HaLow (<NUM>. 11ah), zWave, ZigBee, Wireless M-Bus or other short-range wireless protocol known to one of skill in the art. Using short-range wireless protocols <NUM>, the communication module <NUM> is configured to transmit the sensor data <NUM> to a local gateway device <NUM> and the local gateway device <NUM> is configured to transmit the sensor data <NUM> to a analytic engine <NUM> through a network <NUM>, as described above.

The communication module <NUM> may also allow a sensing apparatus <NUM> to communicate with other sensing apparatus <NUM> either directly through short-range wireless protocols <NUM> or indirectly through the local gateway device <NUM> and/or the cloud computing network <NUM>. Advantageously, this allows the sensing apparatuses <NUM> to coordinate detection of sensor data <NUM>.

The sensing apparatus <NUM> includes an elevation determination module <NUM> configured to determine an elevation or (i.e., height) of a sensing apparatus <NUM> that is located on a moving component of the escalator <NUM>, such as for example the tread plate <NUM>, the step chain <NUM> and/or the handrail <NUM>. The elevation determination module <NUM> may utilize various approaches to determine an elevation or (i.e., height) of the sensing apparatus <NUM>. The elevation determination module <NUM> may be configured to determine an elevation of the sensing apparatus <NUM> using at least one of a pressure elevation determination module <NUM> and an acceleration elevation determination module <NUM>.

The acceleration elevation determination module <NUM> is configured to determine a height change of the sensing apparatus in response to the acceleration of the sensing apparatus <NUM> detected along the Z axis. The sensing apparatus <NUM> may detect an acceleration along the Z axis shown at <NUM> and may integrate the acceleration to get a vertical velocity of the sensing apparatus at <NUM>. At <NUM>, the sensing apparatus <NUM> may also integrate the vertical velocity of the sensing apparatus <NUM> to determine a vertical distance traveled by the sensing apparatus <NUM> during the acceleration data <NUM> detected at <NUM>. The direction of travel of the sensing apparatus <NUM> may also be determined in response to the acceleration data <NUM> detected. The elevation determination module <NUM> may then determine the elevation of the sensing apparatus <NUM> in response to a starting elevation and a distance traveled away from that starting elevation. The starting elevation may be based upon tracking the past operation and/or movement of the sensing apparatus <NUM>. Unusual changes in acceleration and/or the velocity of the escalator may indicate poor CBM health score <NUM>.

The pressure elevation determination module <NUM> is configured to detect an atmospheric air pressure when the sensing apparatus is in motion and/or stationary using the pressure sensor <NUM>. The pressure detected by the pressure sensor <NUM> may be associated with an elevation through either a look up table or a calculation of altitude using the barometric pressure change in two non-limiting embodiments. The direction of travel of the sensing apparatus <NUM> may also be determined in response to the change in pressure detected via the pressure data <NUM>. For example, the change in the pressure may indicate that the sensing apparatus <NUM> is either moving up or down. The pressure sensor <NUM> may need to periodically detect a baseline pressure to account for changes in atmospheric pressure due to local weather conditions. For example, this baseline pressure may need to be detected daily, hourly, or weekly in non-limiting embodiments. In some embodiments, the baseline pressure may be detected whenever the sensing apparatus is stationary, or at certain intervals when the sensing apparatus <NUM> is stationary and/or at a known elevation. The acceleration of the sensing apparatus <NUM> may also need to be detected to know when the sensing apparatus <NUM> is stationary and then when the sensing apparatus <NUM> is stationary the sensing apparatus <NUM> may need to be offset to compensate the sensor drift and environment drift.

In one embodiment, the pressure elevation determination module <NUM> may be used to verify and/or modify an elevation of the sensing apparatus <NUM> determined by the acceleration elevation determination module <NUM>. In another embodiment, the acceleration elevation determination module <NUM> may be used to verify and/or modify an elevation of the sensing apparatus determined by the pressure elevation determination module <NUM>. In another embodiment, the pressure elevation determination module <NUM> may be prompted to determine an elevation of the sensing apparatus <NUM> in response to an acceleration detected by the IMU sensor <NUM>.

The health determination module <NUM> is configured to determine a CBM health score <NUM> of the escalator <NUM>. The CBM health score <NUM> may be associated with a specific component of the escalator <NUM> or be a CBM health score <NUM> for the overall escalator <NUM>. The health determination module <NUM> may be located in the analytic engine <NUM>, local gateway device <NUM>, or the sensing apparatus <NUM>. In an embodiment, the health determination module <NUM> is located in the sensing apparatus <NUM> to perform the edge processing. The health determination module <NUM> may use a FFT algorithm to process the sensor data <NUM> to determine a CBM health score <NUM>. In one embodiment, a health determination module <NUM> may process at least one of the sound data <NUM> detected by the microphone <NUM>, the light detected by the light sensor <NUM>, the humidity detected by the humidity sensor <NUM>, the temperature data detected by the temperature sensor <NUM>, the acceleration data <NUM> detected by the IMU sensor <NUM>, and/or the pressure data <NUM> detected by the pressure sensor <NUM> in order to determine a CBM health score <NUM> of the escalator <NUM>.

In an embodiment, the health determination module <NUM> may process at least one of the sound data <NUM> detected by the microphone <NUM> and the acceleration data <NUM> detected by the IMU sensor <NUM> to determine a CBM health score <NUM> of the escalator <NUM>.

Different frequency ranges may be required to detect different types of vibrations in the escalator <NUM> and different sensors (e.g., microphone, IMU sensor <NUM>,. etc.) of the sensing apparatus <NUM> may be better suited to detect different frequency ranges. In one example, a vibration in the handrail <NUM> may consist of a low frequency contribution vibration of less than <NUM> and a higher frequency vibration that is caused on the point where friction in the handrail <NUM> may be occurring. The low frequency vibration is detected using the IMU sensor <NUM>, whereas the higher frequency vibrations (e.g., in the kHz region) are detected using the microphone <NUM> is more power efficient. Advantageously, using the microphone to detect higher frequency vibrations and the IMU sensor <NUM> to detect lower frequency vibrations is more energy efficient. Higher frequency includes frequencies that are greater than or equal to <NUM>. Lower frequency includes frequencies that are less than or equal to <NUM>.

The sensing apparatus <NUM> may be placed in specific locations to capture vibrations from different components. In an embodiment, the sensing apparatus <NUM> is placed in the handrail <NUM> (i.e., moving with the handrail <NUM>). When located in the handrail <NUM>, the sensing apparatus <NUM> may utilize the IMU sensors <NUM> to capture low frequency vibrations. Any variance in the low frequency vibration from a baseline may indicate a low CBM health score <NUM>. A foreign object (e.g., dirt, dust, pebbles) may get stuck in the handrail <NUM>, thus leading to increased vibration. In one example, low frequency oscillations may appear because of dust or dirt causing friction. These low frequency oscillations may be identified using a low pass filter of less than <NUM>. In another example, single spikes or noise may appear by dirt sticking on tracks or wheels of the step chain <NUM>. These single spikes or noise may be detected by identifying spikes in vibrations greater than <NUM>.

In an embodiment, the sensing apparatus <NUM> is attached to (e.g., in or on) the step chain <NUM> or tread plate <NUM> (i.e., moving with the step chain <NUM> or tread plate <NUM>). In another embodiment not forming part of the invention, the sensing apparatus <NUM> located stationary proximate the drive machine <NUM>. The temperature sensor <NUM> may best measure temperature of the drive machine <NUM> when the sensing apparatus <NUM> is attached to the drive machine <NUM>. The IMU sensor <NUM> may best measure accelerations when the sensing apparatus <NUM> is attached to the output sheave <NUM>. When attached to the step chain <NUM> or located stationary proximate the drive machine <NUM>, the sensing apparatus <NUM> may utilize the IMU sensors <NUM> to capture low frequency vibrations that may indicate a bearing problem with a main pivot of the step chain <NUM>, a step roller of the step chain <NUM>, or a hand rail pivot of the hand rail <NUM>. Alternatively, when attached to the step chain <NUM> or located stationary proximate the drive machine <NUM>, the sensing apparatus <NUM> may utilize the microphone <NUM> to capture high frequency vibrations that may indicate a bearing problem. A FFT algorithm may be utilized to help analyze the high frequency vibrations captured by the microphone. Advantageously, FFT algorithms use pre-defined special electronic hardware resulting in an easy, low cost, and low power consuming way to detect deviations. When attached to the step chain <NUM> or located stationary proximate the drive machine <NUM>, the sensing apparatus <NUM> may utilize the temperature sensor <NUM> to measure temperatures. Increasing temperatures may be indicative of increased machine load on the drive machine <NUM> or increased friction. When attached to the step chain <NUM>, the sensing apparatus <NUM> may utilize the IMU sensors <NUM> to capture accelerations in multiple axis (e.g., X axis, Y axis, and Z axis) to determine tread plate <NUM> direction (e.g., up or down), a 3D acceleration profile of the trade plate <NUM> to determine, amongst other things, when the tread plate <NUM> is turning, a tread plate <NUM> misalignment, and bumps in the step chain <NUM> that may be indicative of foreign objects (dirt, pebbles, dust,. etc.) in the step chain <NUM> or tread plates <NUM>. The combination of multiple sensor information from different sensors of the plurality of sensors <NUM> leads to the ability of the sensor fusion within the sensing apparatus, thus allowing the sensors to work in concert to confirm, adjust, or deny data readings. For example, an increase in acceleration values within the acceleration data <NUM> (at certain frequencies (FFT)) may be associated with an increase in temperature detected by the temperature sensor <NUM> (e.g., machine heat of the drive machine <NUM> due to higher load) and an increase in relative humidity detected by the humidity sensor <NUM> (excluding variations of frictions due to external weather conditions).

The CBM health score <NUM> may be a graded scale indicating the health of the escalator <NUM> and/or components of the escalator <NUM>. In a non-limiting example, the CBM health score <NUM> may be graded on a scale of one-to-ten with a CBM health score <NUM> equivalent to one being the lowest CBM health score <NUM> and a CBM health score <NUM> equivalent to ten being the highest CBM health score <NUM>. In another non-limiting example, the CBM health score <NUM> may be graded on a scale of one-to-one-hundred percent with a CBM health score <NUM> equivalent to one percent being the lowest CBM health score <NUM> and a CBM health score <NUM> equivalent to one-hundred percent being the highest CBM health score <NUM>. In another non-limiting example, the CBM health score <NUM> may be graded on a scale of colors with a CBM health score <NUM> equivalent to red being the lowest CBM health score <NUM> and a CBM health score <NUM> equivalent to green being the highest CBM health score <NUM>. The CBM health score <NUM> may be determined in response to at least one of the acceleration data <NUM>, the pressure data <NUM>, and/or the temperature data. For example, acceleration data <NUM> above a threshold acceleration (e.g., normal operating acceleration) in any one of the X axis, a Y axis, and a Z axis may be indicative of a low CBM health score <NUM>. In another example, elevated temperature data above a threshold temperature for components may be indicative of a low CBM health score <NUM>. In another example, elevated sound data <NUM> above a threshold sound level for components may be indicative of a low CBM health score <NUM>.

Referring now to <FIG>, while referencing components of <FIG>. <FIG> shows a flow chart of a method <NUM> of monitoring an escalator, in accordance with an embodiment of the disclosure. In an embodiment, may be performed by the escalator health monitoring system <NUM>. In another embodiment, the method <NUM> may be performed by at least one of the sensing apparatus <NUM>, the local gateway device <NUM>, and the analytic engine <NUM>.

At block <NUM>, acceleration data <NUM> of an escalator <NUM> is detected using an inertial measurement unit sensor <NUM> located in a sensing apparatus <NUM>. In one embodiment, the sensing apparatus <NUM> is located within a handrail <NUM> of the escalator <NUM> and moves with the handrail <NUM>. In another embodiment, the sensing apparatus <NUM> is attached to a step chain <NUM> of the escalator <NUM> and moves with the step chain <NUM>. In another embodiment that does not form part of the invention, the sensing apparatus <NUM> is attached to a tread plate <NUM> of the escalator <NUM> and moves with the tread plate <NUM>. In another embodiment that does not form part of the invention, the sensing apparatus <NUM> is stationary and located proximate to a step chain <NUM> of the escalator <NUM> or a drive machine <NUM> of the escalator <NUM>. At block <NUM>, sound data <NUM> of the escalator <NUM> is detected using a microphone <NUM> located in the sensing apparatus <NUM>.

At block <NUM>, a CBM health score <NUM> is determined in response to at least one of the acceleration data <NUM> and the sound data <NUM>. Alternatively, the CBM health score <NUM> may be determined in response to at least the acceleration data <NUM>. In one embodiment, the sensing apparatus <NUM> is configured to determine the CBM health score <NUM> in response to at least one of the acceleration data <NUM> and the sound data <NUM>.

In another embodiment, the acceleration data <NUM> and the sound data <NUM> is transmitted to a local gateway device <NUM> in wireless communication with the sensing apparatus <NUM> through a short-range wireless protocol <NUM> and the local gateway device <NUM> is configured to determine a CBM health score <NUM> in response to at least one of the acceleration data <NUM> and the sound data <NUM>.

In another embodiment, the acceleration data <NUM> and the sound data <NUM> is transmitted to a local gateway device <NUM> in wireless communication with the sensing apparatus <NUM> through a short-range wireless protocol <NUM> and the local gateway device <NUM> transmits the acceleration data <NUM> and the sound data <NUM> to an analytic engine <NUM> through a cloud computing network <NUM>. The analytic engine <NUM> is configured to determine a CBM health score <NUM> in response to at least one of the acceleration data <NUM> and the sound data <NUM>.

Low frequency vibrations less than <NUM> are detected using the inertial measurement unit sensor <NUM>. High frequency vibrations greater than <NUM> are using the microphone <NUM>. In another embodiment, high frequency vibrations are between <NUM> and <NUM>. In another embodiment, high frequency vibrations are greater than <NUM>.

As described above, embodiments can be in the form of processorimplemented processes and devices for practicing those processes, such as processor. Embodiments can also be in the form of computer program code (e.g., computer program product) containing instructions embodied in tangible media (e.g., non-transitory computer readable medium), such as floppy diskettes, CD ROMs, hard drives, or any other non-transitory computer readable 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 can also be in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an device for practicing the exemplary embodiments.

Those of skill in the art will appreciate that various example embodiments are shown and described herein, each having certain features in the particular embodiments, but the present disclosure is not thus limited.

Claim 1:
A monitoring system (<NUM>) for monitoring an escalator (<NUM>), the monitoring system comprising:
a local gateway device (<NUM>); and
a sensing apparatus (<NUM>) in wireless communication with the local gateway device through a short-range wireless protocol (<NUM>), the sensing apparatus comprising:
an inertial measurement unit sensor (<NUM>) configured to detect acceleration data (<NUM>) of the escalator; and
a microphone (<NUM>) configured to detect sound data (<NUM>) of the escalator,
wherein the sensing apparatus uses the inertial measurement unit sensor to detect low frequency vibrations less than <NUM>,
wherein the sensing apparatus uses the microphone to detect high frequency vibrations greater than <NUM>,
characterised in that the sensing apparatus is located within a handrail (<NUM>) of the escalator (<NUM>) and moves with the handrail, or
in that the sensing apparatus is attached to a step chain (<NUM>) of the escalator and moves with the step chain.