Escalator monitoring system, method, sound data collection device and fixture therefor

Abstract

An escalator monitoring system comprises a data collection device (1) arranged near a component of an escalator that needs to be monitored, so as to collect data of the component related to the operation of the escalator; a data sending device (4) for sending the collected data; and a cloud processor (5) for receiving the data, comparing the data with the threshold data of the component obtained under normal operating conditions stored in a database of the cloud processor (5), and responding based on the comparison result. The escalator monitoring system can collect and analyze data in any environment, and does not rely on subjective judgment of manpower, and predicts possible faults in advance without stopping the operation of the escalator, saving maintenance time, reducing related costs, and improving the safety and riding comfort of the escalator.

Description

[0001] This application is a divisional application of the invention patent application with the application date of December 29, 2017, application number 201780097574.7, and invention name "Escalator monitoring system, method, sound data collection device and clamp therefor". Technical Field

[0002] The present invention relates to an escalator monitoring system, an escalator detection method, a sound data collection device and a fixture therefor, and more specifically, to an escalator monitoring system or an escalator detection method, a sound data collection device and a fixture therefor that use data collected by the sound data collection device to analyze the operating status of the escalator. Background Art

[0003] Escalators are widely used in many occasions, such as department stores, office buildings and public facilities, as well as various indoor or outdoor environments. The safe and smooth operation of escalators requires monitoring the operating status of related components, which is closely related to the application of the escalator and the temperature, humidity and dust of the environment, as well as the number of users and the frequency of use. Therefore, the status of each escalator is different, and frequent on-site inspection and maintenance of related components are required to keep them running correctly. This involves a lot of work and makes it difficult for manufacturers or users of escalators to keep track of their operating status in an organized manner.

[0004] To solve this problem, it is necessary to improve the escalator monitoring system so that it allows remote monitoring and generates raw data of components related to the smooth operation of the escalator. In addition, the system can process and analyze the raw data, quickly determine the operation of the escalator, and can provide early warning of possible escalator failures without relying on manpower, and can provide manufacturers and users with data generated during the operation of the escalator for reference. Summary of the invention

[0005] The object of the present invention is to provide an escalator monitoring system, a data collection device and a fixture for the monitoring system, and an escalator monitoring method that meet the above requirements.

[0006] According to a first aspect of the present invention, there is provided an escalator monitoring system, which includes a data collection device, which is arranged near the components of the escalator that need to be monitored, and is used to collect data of the components; a data sending device, which is used to send data related to the safe operation of the escalator; and a cloud processor, which is used to receive the data related to the safe operation of the escalator, compare it with thresholds stored in the cloud processor and obtained from the components under normal operating conditions, and respond based on the comparison result.

[0007] Preferably, the data collection device is a sound data collection device, a temperature collection device, a power collection device or a vibration collection device.

[0008] Preferably, the escalator monitoring system further comprises a local data processing device, and the data related to the safe operation of the escalator is data processed by the local data processing device.

[0009] Preferably, the local data processing device calculates characteristic values ​​of the data as data related to the safe operation of the escalator.

[0010] Preferably, if the comparison result is greater than the threshold value, the local data processing device issues an alarm signal regarding the safe operation of the escalator, or sends the comparison result to a cloud processor.

[0011] Preferably, the data related to the safe operation of the escalator are data collected by a data collection device, and the cloud processor receives and processes the data, and then compares the data with thresholds stored in the cloud processor and obtained from components under normal operating conditions.

[0012] Preferably, the cloud processor calculates characteristic values ​​of the data as data related to the safe operation of the escalator, and then compares it with threshold values ​​stored in the cloud processor and obtained from components under normal operating conditions.

[0013] Preferably, if the comparison result is greater than the threshold value, the cloud processor sends out an alarm signal regarding the safe operation of the escalator, and triggers the customer service system or directly communicates with the maintenance personnel to perform lubrication maintenance.

[0014] Preferably, the cloud processor includes an escalator database for storing escalator operation history data, escalator parameters and escalator maintenance history.

[0015] Preferably, the cloud processor compares the processed data with the threshold value through statistical, analytical, artificial intelligence and machine learning methods.

[0016] Preferably, the cloud processor stores data of the components related to the safe operation of the escalator.

[0017] Preferably, if the comparison result is greater than the threshold value, the cloud processor sends out an alarm signal about the safety status of the escalator and triggers the customer service system.

[0018] Preferably, the local data processing device is a microcomputer or a DSP chip.

[0019] Preferably, the microcomputer transmits data to the cloud processor via a wireless network.

[0020] Preferably, the data collection device is a sound data collection device, and the local data processing device obtains sound data related to the safe operation of the escalator of components in the range of [F low, F high] through a bandpass filter, calculates the root mean square value of the data, and sends the root mean square value to the cloud processor for comparison with the sound data threshold of the metal component stored in the database of the cloud processor and obtained when the metal component is operating under normal operating conditions.

[0021] Preferably, the data collection device is a sound data collection device, which is arranged near the contact position of the component, that is, the contact position of the drive device and the drive chain, the contact position of the drive chain and the drive sprocket, or the contact position of the step guide block and the skirt plate, and is used to calculate the characteristic value of the data and send the characteristic value to the cloud processor for comparison with the sound data threshold of the metal component stored in the database of the cloud processor and obtained when the component is operating under normal operating conditions.

[0022] Preferably, the data collection device is a sound data collection device, the cloud processor receives the data sent by the data collection device, obtains the data related to the safe operation of the escalator of the contact parts in the range of [F low, F high] through a bandpass filter, calculates the root mean square value of the data, and compares the root mean square value with the sound data threshold of the metal parts stored in the database of the cloud processor and obtained when the parts are operating under normal operating conditions.

[0023] Preferably, the data collection device is a sound data collection device, the cloud processor receives the sound data sent by the data collection device, calculates the characteristic value of the data, and compares the characteristic value with the sound data threshold of the metal part stored in the database of the cloud processor and obtained when the part is operating under normal operating conditions.

[0024] Preferably, if the sound data are from a group of escalators at the same location and have similar sound data patterns, the local data processing device may select only one of the sound data for processing.

[0025] Preferably, the data collection device is a temperature sensor assembly, which is used to detect the ambient temperature and the temperature of the back of the handrail when the escalator is in a stopped state, a standby state and a full-speed running state; the data sending device receives the ambient temperature and the temperature of the back of the handrail from the temperature sensor assembly, and sends the ambient temperature and the temperature of the back of the handrail to a local data processing device or a cloud processor at a predetermined frequency, the local data processing device or the cloud processor compares the ambient temperature and the temperature of the back of the handrail and obtains the temperature difference between the ambient temperature and the temperature of the back of the handrail, and wherein the predetermined temperature difference threshold is stored in the local data processing device or the cloud processor, if the temperature difference between the ambient temperature and the temperature of the back of the handrail exceeds the predetermined temperature difference threshold, the local data processing device or the cloud processor will issue an alarm signal.

[0026] Preferably, the data collection device is a temperature sensor assembly, which is used to detect the temperature of the back side of the handrail and the ambient temperature of the escalator within a predetermined time period. The data sending device sends the detected ambient temperature and the temperature of the back side of the handrail to a local data processing device or a cloud processor. The local processing device or the cloud processor compares the ambient temperature with the temperature of the back side of the handrail to obtain a first average value and a first variance value of the temperature difference between the ambient temperature and the temperature of the back side of the handrail within the first predetermined time period.

[0027] Preferably, the temperature sensor assembly also detects the ambient temperature and the temperature of the back of the armrest within another predetermined time period, and the data sending device sends the detected ambient temperature and the temperature of the back of the armrest to a local data processing device or a cloud processor, and the local data processing device or the cloud processor compares the ambient temperature with the temperature of the back of the armrest to obtain a second average value and a second variance value of the temperature difference between the temperature of the back of the armrest and the ambient temperature within the said another predetermined time period.

[0028] Preferably, the local data processing device or cloud processor is configured to issue a first alarm signal when the difference between the second average value and the first average value is greater than k times the first variance value, wherein k is an integer greater than 1 set in the local data processing device or the cloud processor.

[0029] Preferably, the local data processing device or cloud processor further determines the relationship between the first variance value and a predetermined first threshold, and the relationship between the second variance value and a predetermined second threshold. If the first variance value is less than the first threshold, and the second variance value is less than the second threshold, the local data processing device or cloud processor will issue a second alarm signal.

[0030] Preferably, the data collection device is a passenger flow sensor and a power sensor, the passenger flow sensor is used to detect the passenger flow entering the escalator per unit time, and the power sensor is used to detect the power consumption per unit time. The data sending unit sends the detected passenger flow per unit time and the detected power consumption per unit time to a local data processing device or a cloud processor. The local data processing device or the cloud processor judges the operating status of the escalator based on the relationship between the passenger flow per unit time and the power consumption per unit time to determine whether an alarm signal needs to be issued.

[0031] Preferably, the local data processing device or cloud processor calculates the ratio of power consumption per unit time to passenger flow per unit time. If the ratio has an abnormal mutation, the local data processing device or cloud processor will issue an alarm signal; if the ratio has no abnormal mutation but has an increasing trend within a predetermined time and is greater than a predetermined threshold, the local data processing device or cloud processor will issue an alarm signal.

[0032] Preferably, when the escalator is running at idle speed, the power sensor detects the average power within a predetermined period of time. If the average power does not fall within a predetermined threshold range stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor will issue an alarm signal.

[0033] Preferably, the local data processing device or cloud processor is also configured to issue an alarm signal if the passenger flow detected by the passenger flow sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the passenger flow is zero within a time period exceeding a predetermined threshold.

[0034] Preferably, the local data processing device or cloud processor is also configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is the idle power output within a period exceeding a predetermined threshold.

[0035] According to a second aspect, an escalator monitoring system is provided, which includes a sound data collection device, which is arranged near the components of the escalator that need to be monitored, and is used to collect data of the components; a local data processing device, which is used to process the sound data locally, send the sound data to a cloud processor, or respond to the results after processing the sound data locally.

[0036] According to a third aspect, an escalator monitoring system is provided, comprising a cloud processor for receiving sound data sent by a remote data collection device disposed near a component of the escalator that needs to be monitored, processing the sound data, and responding to a result after processing the data.

[0037] According to a fourth aspect, a sound data collection device is provided, which includes a circuit board, one or more digital microphones, and a circuit box enclosing the circuit board and the digital microphones, the circuit box including a top wall, a bottom wall and side walls, at least one of the top wall and the bottom wall being provided with a sound pickup hole.

[0038] Preferably, a waterproof membrane is provided on the inner side or the outer side of the wall where the sound pickup hole is located to prevent water or moisture from entering the digital microphone.

[0039] Preferably, the digital microphone is arranged on the inner side of the wall where the sound pickup hole is located, arranged below the waterproof membrane, or arranged on the circuit board.

[0040] According to a fifth aspect, there is provided a fixture for a temperature collecting device of an escalator monitoring system, wherein a mounting position of the fixture can be adjusted according to a driving mode of a handrail.

[0041] Preferably, the clamp is mounted on a C-shaped piece of the skirt of the escalator if the handrail is in friction wheel drive mode.

[0042] Preferably, the clamp comprises a bracket and an adjustable plate, the adjustable plate is mounted on the bracket, the bracket is mounted to a C-shaped piece of the skirt plate, and the temperature sensor assembly is mounted on an end of the adjustable plate away from the C-shaped piece.

[0043] Preferably, one of the adjustable plate and the bracket has a first longitudinal hole extending along the length direction, and the other of the adjustable plate and the bracket has an opening, and a connecting device passes through the first longitudinal hole and the opening to connect the adjustable plate and the bracket, and according to the alignment position of the opening along the length direction with the first longitudinal hole, the distance between the temperature sensor assembly and the C-shaped member along the length direction is adjusted.

[0044] Preferably, the clamp is mounted on a support column of the escalator when the handrail is in newel-wheel drive mode.

[0045] Preferably, the clamp comprises a bracket and an adjustable plate, the adjustable plate is mounted on the bracket, the bracket is mounted on the support column, and the temperature sensor assembly is mounted on an end of the adjustable plate away from the support column.

[0046] Preferably, one of the adjustable plate and the bracket has a second longitudinal hole extending in the width direction, and the other of the adjustable plate and the bracket has a third longitudinal hole extending in the length direction, and the connecting device passes through the second longitudinal hole and the third longitudinal hole to connect the adjustable plate and the bracket, and according to the alignment position of the second longitudinal hole and the third longitudinal hole, the distance between the temperature sensor assembly and the pillar in the length direction and the distance between the temperature sensor assembly and the armrest in the width direction are adjusted.

[0047] According to a sixth aspect, an escalator is provided, comprising the escalator monitoring system as described above, the sound data collection device as described above and / or the clamp as described above.

[0048] According to a seventh aspect, there is provided a method for monitoring an escalator, comprising the following steps:

[0049] The temperature sensor assembly is used to detect the back temperature of the handrail and the ambient temperature when the escalator is in a stopped state, a standby state and a full-speed running state;

[0050] Using a data transmission unit to receive the ambient temperature and the temperature of the back of the armrest from the temperature sensor assembly, and sending the ambient temperature and the temperature of the back of the armrest to a local data processing device or a cloud processor at a predetermined frequency;

[0051] Wherein, the local data processing device or the cloud processor compares the ambient temperature and the temperature of the back of the armrest and obtains the temperature difference between the ambient temperature and the temperature of the back of the armrest, and wherein the predetermined temperature difference threshold is stored in the local data processing device or the cloud processor, and if the temperature difference between the ambient temperature and the temperature of the back of the armrest exceeds the predetermined temperature difference threshold, the local data processing device or the cloud processor will issue an alarm signal.

[0052] Preferably, the method also includes: using the local data processing device or the cloud processor to capture the back temperature of the armrest and the ambient temperature detected by the temperature sensor component within a predetermined time period, and comparing the ambient temperature with the back temperature of the armrest to obtain a first average value and a first variance value of the temperature difference between the ambient temperature and the back temperature of the armrest within the predetermined time period.

[0053] Preferably, the method also includes: using the local data processing device or the cloud processor to capture the temperature of the back of the armrest and the ambient temperature detected by the temperature sensor component within another predetermined time period, and comparing the ambient temperature with the temperature of the back of the armrest to obtain a second average value and a second variance value of the temperature difference between the ambient temperature and the temperature of the back of the armrest within the other predetermined time period.

[0054] Preferably, the local data processing device or cloud processor is configured to send out a first alarm signal when the difference between the second average value and the first average value is greater than k times the first variance value, wherein k is an integer greater than 1 set in the local data processing device or cloud processor.

[0055] Preferably, the local data processing device or cloud processor further determines the relationship between the first variance value and a predetermined first threshold, and the relationship between the second variance value and a predetermined second threshold. If the first variance value is less than the first threshold and the second variance value is less than the second threshold, the local data processing device or cloud processor will issue a second alarm signal.

[0056] According to an eighth aspect, there is provided a method for monitoring an escalator, comprising the following steps:

[0057] A passenger flow sensor is used to detect the passenger flow entering the escalator per unit time;

[0058] Use power sensors to detect power consumption per unit time;

[0059] The data transmission unit transmits the detected passenger flow within a unit time and the detected power consumption within a unit time to a local data processing device or a cloud processor;

[0060] The server determines the running state of the escalator according to the relationship between the passenger flow rate per unit time and the power consumption per unit time to determine whether an alarm signal needs to be issued.

[0061] Preferably, the local data processing device or cloud processor calculates the ratio of power consumption per unit time to passenger flow per unit time. If the ratio has an abnormal mutation, the local data processing device or cloud processor will issue an alarm signal; if the ratio has no abnormal mutation but has an increasing trend within a predetermined time and is greater than a predetermined threshold, the local data processing device or cloud processor will issue an alarm signal.

[0062] Preferably, when the escalator is running at idle speed, the power sensor detects the average power within a predetermined period of time. If the average power does not fall within a predetermined threshold range stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor will issue an alarm signal.

[0063] Preferably, the local data processing device or cloud processor is also configured to issue an alarm signal if the passenger flow detected by the passenger flow sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the passenger flow is zero within a time period exceeding a predetermined threshold.

[0064] Preferably, the local data processing device or cloud processor is also configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is the idle power output within a period exceeding a predetermined threshold.

[0065] The automatic escalator monitoring system or automatic escalator detection method according to the present invention can obtain and analyze data in any environment, including indoors or outdoors, quiet and noisy environments, and does not rely on human subjective judgment. It also accurately predicts failures in advance without downtime, saves maintenance time and money, and improves the safety and riding comfort of elevators. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flow chart of an escalator monitoring system according to a first embodiment of the present invention.

[0067] Figure 2 The Spectrogram function plots a spectrum diagram by way of example showing a process of filtering out low-frequency noise and high-frequency noise through a bandpass filter.

[0068] Figure 3 A comparison of the root mean square value of the sound data calculated in the first embodiment and the threshold value is shown.

[0069] Figure 4 is a flow chart of an escalator monitoring system according to a second embodiment of the present invention.

[0070] Figure 5 It is the sound data pattern of different classifications of faults.

[0071] Figure 6 It is a schematic diagram of the analysis process of classifier 8.

[0072] Figure 7 is a flow chart of an escalator monitoring system according to a third embodiment of the present invention.

[0073] Figure 8 is a flow chart of an escalator monitoring system according to a fourth embodiment of the present invention.

[0074] Fig. 9 is a flow chart of an escalator monitoring system according to a fifth embodiment of the present invention.

[0075] Fig.10 is a flow chart of an escalator monitoring system according to a sixth embodiment of the present invention.

[0076] Fig.11 It is the first embodiment of the sound data collection device 1 used in the escalator monitoring system of various embodiments of the present invention.

[0077] Fig.12 This is the second embodiment of the sound data collection device 1 used in the escalator monitoring system of each embodiment of the present invention.

[0078] Fig.13 This is the third embodiment of the sound data collection device 1 used in the escalator monitoring system of each embodiment of the present invention.

[0079] Fig.14 This is the fourth embodiment of the sound data collection device 1 used in the escalator monitoring system of various embodiments of the present invention.

[0080] Fig.15 is a block diagram of an escalator monitoring system according to a seventh embodiment of the present invention;

[0081] Fig.16 The friction wheel drive mode is shown;

[0082] Fig.17 End wheel drive mode is shown;

[0083] Fig.18 Shows the fixture mounted on the C-shaped piece of the skirt in the friction wheel drive mode;

[0084] Fig.19 Show Fig.18 A magnified view of A in FIG.

[0085] Fig. 20 Show Fig.18 Schematic diagram of the fixture;

[0086] Fig.21 Shown Fig.18 A fixture is used to fix the temperature sensor assembly on the escalator;

[0087] Fig. 22 The clamp is shown mounted on the strut in end wheel drive mode;

[0088] Fig.23 Show Fig.18 An enlarged view of B in FIG.

[0089] Fig.24 Show Fig. 22 Schematic diagram of the fixture;

[0090] Fig.25 Shown Fig. 22 The temperature sensor assembly is fixed to the escalator by a fixture.

[0091] Fig.26 A flow chart showing a first embodiment of a monitoring method according to the present invention.

[0092] Fig. 27is a block diagram of an escalator monitoring system according to an eighth embodiment of the present invention.

[0093] Fig.28 Shows the ratio of power output per unit time to passenger flow per unit time.

[0094] Fig.29 A schematic diagram showing the relationship between passenger flow and motor power of the real-time monitoring device of the present invention;

[0095] Fig.30 A schematic diagram showing another relationship between passenger flow and motor power of the real-time monitoring device of the present invention;

[0096] Fig.31 A flow chart showing a second embodiment of the monitoring method according to the present invention. DETAILED DESCRIPTION

[0097] The escalator monitoring system of the present invention avoids the occurrence of escalator failures through a data collection device, performs predictive maintenance on escalator failures, and thus reduces the manpower and cost of escalator maintenance. The data collection device at or near the components of the escalator related to the safety and normal operation of the escalator collects data when the escalator is running, analyzes, judges, predicts and determines the operating status of the escalator, and responds to the determined operating status.

[0098] The escalator monitoring system of the present invention can monitor the vibration state of the escalator drive device or drive member, the lubrication state between the key components of the escalator that contact each other, the temperature of the key components of the escalator, the motor power of the escalator related to the changing passenger flow, etc. The drive device or drive member that needs to monitor the vibration state includes the gear box, steps, etc. of the escalator. The vibration of the gear box and the steps is different in normal operation and abnormal state, and the vibration data reflecting the vibration difference can be used to analyze, predict and determine the operation state of the escalator, thereby realizing predictive maintenance. The components that need to monitor the lubrication state include the drive device and the drive chain, the drive chain and the drive sprocket, and the step guide block and the skirt. These components are metal and move relative to each other. When there is sufficient lubrication between them, these metal components move smoothly and emit a uniform and small sound. In the state of lack of lubricating oil or rusty and dusty, the movement will become less smooth and produce harsh noise. Therefore, the sound data collected therefrom can be used to monitor the adequacy of lubrication, thereby providing predictive maintenance by analyzing, predicting and determining the operation status of the escalator. Thermometers can be placed at key components of escalators to monitor their temperature changes, which helps analyze, predict, and determine the operating conditions of escalators, thereby providing predictive maintenance. Key components include, but are not limited to, handrails. When their temperature varies within the normal range, the handrails will operate normally; otherwise, they may malfunction. Passenger flow affects the normal operation of escalators. High passenger flow that exceeds the capacity of escalators can cause safety issues. And, passenger flow can be evaluated based on the motor power of the escalator. Therefore, the motor power can be used to analyze, predict, and determine the operating conditions of escalators, thereby providing predictive maintenance.

[0099] The present invention is described in further detail in the following examples.

[0100] Figure 1It is a flow chart of an escalator monitoring system according to the first embodiment. In the first embodiment of the present invention, the escalator monitoring system monitors the lubrication state of the escalator, and uses a data collection device to collect sound data emitted by metal parts, and analyzes the sound data to determine whether the lubrication state is normal. The data collection device is usually a digital microphone module, which is arranged near the contact position between the driving device and the driving chain of the escalator, near the contact position between the driving chain and the driving sprocket of the escalator, or near the contact position between the step guide block and the skirt board under the front cover of the step belt. It can also be arranged on the truss of the escalator close to the contact position between the motor and the driving chain, or at a position near any contact and therefore need to be monitored. The microphone module can use an ordinary microphone or an ultrasonic microphone commonly used in mobile phones. It can record sound waves and support playback function. The digital microphone module continuously collects sound data and stores sound files, which can be played back when needed.

[0101] Figure 1 In the embodiment, the sound data collecting device 1 collects sound data at the contact position between the driving device and the driving chain, at the contact position between the driving sprocket of the gear box of the escalator and the driving chain, or at the contact position between the step guide block and the skirt plate under the front cover of the step belt. The sound file containing the sound data is stored at the local data processing device 2 of the local device 6 and processed by the bandpass filter 3. Then, the file is sent to the cloud processor 5 through the sound data sending device 4.

[0102] At the bandpass filter 3, the sound data is de-noised to obtain the sound data in a specific frequency range [F 低 ,F 高 ] sound data, F 低 Usually between 1000 and 5000 Hz, F 高 Usually greater than 10000Hz, because the sound data in this range is most likely related to the noise generated by the friction between metal and metal. This process can be done by drawing a spectrum graph using the Spectrogram function.

[0103] Figure 2 The Spectrogram function schematically illustrates the process of filtering out low-frequency noise and high-frequency noise through a bandpass filter and plotting a spectrum diagram. Figure 2 (a) shows the sound signal generated by the contact between a normal chain without rust and a sprocket, which is mainly concentrated in the low frequency range. Figure 2 (b) shows the acoustic signal generated by the contact between the rusted chain and the sprocket, which is a mixture of low-frequency noise and high-frequency peaks. Figure 2(c) is the separation of the sound modes related to the rust condition by applying a bandpass filter in the frequency range [5000, 15000]. Figure 2 (c) shows the sound modes separated after applying a bandpass filter with a frequency range of [5000, 15000] and retaining only those related to the degree of rust present on the chain.

[0104] The next step is to calculate the key performance indicator (KPI) of the filtered sound data. A KPI related to the noise emitted by the contact between metal parts can be the root mean square (RMS) value of the sound data. In this embodiment, the RMS value is used to calculate the peak value of the filtered sound data.

[0105] The RMS value is sent to the cloud processor 5 via the sound data sending device 4. In the cloud processor 5, the RMS value is compared with a set threshold value. If the RMS value is higher than the threshold value, the cloud processor responds by sending an alarm signal to the customer service center or directly communicating with the maintenance personnel to perform lubrication maintenance. Figure 3 This comparison is shown exemplarily in .

[0106] The predetermined threshold value may be obtained by testing under various lubrication conditions or by other kinds of experiments.

[0107] Figure 4 is a flow chart of an escalator monitoring system according to the second embodiment.

[0108] In the second embodiment, the digital collection device 1 collects sound data at the contact position between the drive device and the drive chain, at the contact position between the drive sprocket of the gear box of the escalator and the drive chain, or at the contact position between the step guide block and the skirt under the front cover of the step belt. The sound file containing the sound data is stored at the local data processing device 2 of the local device 6. The local data processing device 2 calculates the feature value of the sound data and then sends it to the cloud processor 5 through the sound data sending device 4.

[0109] Figure 5 A comparison between the acoustic data for different fault types is shown. Figure 5 (a) shows the sound data pattern generated when step misalignment occurs, Figure 5 (b) shows the sound data pattern produced by a rusted transmission chain, Figure 5 (c) shows the sound data pattern when the escalator is in normal operation. Obviously, different types of fault sound data have different patterns and therefore have different feature values.

[0110] In this embodiment, the local data processing device 2 calculates the characteristic value of the sound data. The characteristic value can be zero crossing rate, energy, energy entropy, spectrum centroid, spectrum entropy, spectrum flux, spectrum roll-off, spectrum cepstral coefficient (MFCC), chroma vector or chroma deviation.

[0111] The calculated feature value is then sent to the cloud processor 5 through the data sending device 4. At the cloud processor 5, the classifier 8 is trained by using historical or stored sound data of different fault classifications. The classifier 8 adopts a neural network algorithm and adjusts the feature vector to better represent the feature values ​​of different fault classifications.

[0112] Figure 6 is a schematic diagram of the analysis process of the classifier 8. The classifier 8 transmits the received calculated feature values ​​to three neurons, each of which performs analysis and calculation for different fault classifications, outputs a prediction confidence interval for each fault classification, and outputs an analysis result based on the confidence interval, which indicates which fault classification may or may not occur.

[0113] Figure 7 is a flow chart of an escalator monitoring system according to a third embodiment of the present invention.

[0114] In the third embodiment, the digital collection device 1 collects sound data at the contact position between the driving device and the driving chain of the escalator, at the contact position between the driving sprocket and the driving chain of the escalator, or at the contact position between the step guide block and the skirt plate under the front cover of the step belt. The sound file containing the sound data is sent to the cloud processor 5 through the data sending device 4. At the cloud processor 5, the sound data is denoised by the bandpass filter 3 to obtain the sound data in the specific frequency range [F 低 ,F 高 ] in the sound data. 低 Usually around 1000~5000Hz, F 高 Typically greater than 10,000 Hz. The cloud processor 5 then calculates the key performance indicator (KPI) of the filtered sound data. The KPI related to the noise generated by the contact between the metal parts can be the root mean square (RMS) value of the sound data. In this embodiment, the RMS value is used to calculate the peak value of the filtered sound data. The cloud processor 5 then compares the RMS value with a predetermined threshold. If the RMS is higher, the cloud processor will respond by sending an alarm signal to the customer service center, or by communicating directly with the maintenance personnel for lubrication maintenance. Figure 3 This comparison is exemplarily shown in . The predetermined threshold value may be obtained by testing under various lubrication conditions or by other kinds of experiments.

[0115] Figure 8is a flow chart of an escalator monitoring system according to a fourth embodiment of the present invention.

[0116] In the fourth embodiment, the digital collection device 1 is a sound data collection device, which collects sound data at the contact position between the drive device and the drive chain, at the contact position between the drive sprocket of the gear box of the escalator and the drive chain, or at the contact position between the step guide block and the skirt plate under the front cover of the step belt. The sound file containing the sound data is sent to the cloud processor 5 through the data sending device 4. At the cloud processor 5, the classifier 8 is trained by using the historical or stored sound data of different fault classifications. The classifier 8 adopts a neural network algorithm and adjusts the feature vector to better represent the feature values ​​of different fault classifications.

[0117] In this embodiment, the cloud processor 5 calculates the characteristic value of the sound data. The characteristic value may be zero crossing rate, energy, energy entropy, spectrum centroid, spectrum entropy, spectrum flux, spectrum roll-off, MFCC, chroma vector or chroma deviation.

[0118] Figure 5 A comparison between the acoustic data for different fault types is shown. Figure 5 (a) shows the sound data pattern generated when step misalignment occurs, Figure 5 (b) shows the sound data pattern produced by a rusted transmission chain, Figure 5 (c) shows the sound data pattern when the escalator is in normal operation. Obviously, different types of fault sound data have different patterns and therefore have different feature values.

[0119] Figure 6 is a schematic diagram of the analysis process of the classifier 8. The classifier 8 transmits the received calculated feature values ​​to three neurons, each of which performs analysis and calculation for different fault classifications, outputs a prediction confidence interval for each fault classification, and outputs an analysis result based on the confidence interval.

[0120] The third and fourth embodiments differ from the first and second embodiments mainly in that the data processing in the local device 6 in the first and second embodiments is performed in the cloud processor 5 in the third and fourth embodiments. However, this may increase the cost associated with data transmission from the local device 6 to the cloud processor 5.

[0121] Fig. 9 is a flow chart of an escalator lubrication condition monitoring system according to the fifth embodiment.

[0122] In the fifth embodiment, the digital collection device 1 collects sound data at the contact position between the drive device and the drive chain, at the contact position between the drive sprocket of the gear box of the escalator and the drive chain, or at the contact position between the step guide block and the skirt plate under the front cover of the step belt. The sound file containing the sound data is denoised by the bandpass filter 3 to obtain the sound data in the specific frequency range [F 低 ,F 高 ] in the sound data. 低 Usually around 1000~5000Hz, F 高 Typically greater than 10,000 Hz. Then, the local data processing device 2 calculates a key performance indicator (KPI) of the filtered sound data. A KPI related to the noise generated by the contact between the metal parts may be a root mean square (RMS) value of the sound data. In this embodiment, the RMS value is used to calculate the peak value of the filtered sound data. In this embodiment, the local data processing device 2 also compares the RMS value with a predetermined threshold. Figure 3 The comparison process is shown in FIG. 1 . If the RMS is higher, the local data processing device 2 sends a response 7, which may be an alarm signal. The alarm signal is transmitted to the customer service center, or the maintenance personnel are directly contacted to perform escalator lubrication maintenance. The local data processing device 2 may also only send the RMS value to the cloud processor. The predetermined threshold value may be obtained by testing under various lubrication conditions or by other types of experiments.

[0123] Fig.10 is a flow chart of an escalator monitoring system according to a sixth embodiment of the present invention.

[0124] In the sixth embodiment, the digital collection device 1 collects sound data at the contact position between the drive device and the drive chain, at the contact position between the drive sprocket of the gear box of the escalator and the drive chain, or at the contact position between the step guide block and the skirt under the front cover of the step belt. The local data processing device 2 located at the local 6 directly calculates the feature value of the sound data. The classifier 8 is trained by using historical or stored sound data of different fault classifications. The classifier 8 adopts a neural network algorithm and adjusts the feature vector to better represent the feature values ​​of different fault classifications. In this embodiment, the feature value can be zero crossing rate, energy, energy entropy, spectral centroid, spectral entropy, spectral flux, spectral roll-off, spectral cepstrum coefficient (MFCC), chromaticity vector or chromaticity deviation.

[0125] Figure 5 A comparison between the acoustic data for different fault types is shown. Figure 5 (a) shows the sound data pattern generated when step misalignment occurs, Figure 5 (b) shows the sound data pattern produced by a rusted transmission chain, Figure 5 (c) shows the sound data pattern when the escalator is in normal operation. Obviously, different types of fault sound data have different patterns and therefore have different feature values.

[0126] Figure 6 : is a schematic diagram of the analysis process of the classifier 8. The classifier 8 transmits the received calculated feature values ​​to three neurons, each of which performs analysis and calculation for different fault classifications, outputs a predicted confidence interval for each fault classification, and outputs the analysis result based on the confidence interval. The local data processing device can respond to the result. The response can be an alarm signal, which can be sent to the customer service center, or directly contact the maintenance personnel to perform escalator lubrication maintenance. It can also be directly sent to the cloud processor.

[0127] The escalator monitoring system according to the present invention can collect and analyze data in any environment, including indoors or outdoors, quiet and noisy environments, and does not rely on human subjective judgment. It also predicts possible failures without stopping the machine, saving maintenance time and reducing related costs, and improving the safety and riding comfort of the elevator.

[0128] Fig.11 It is a first embodiment of the sound data collection device 1 for the escalator monitoring system of each embodiment of the present invention. In this embodiment, the sound data collection device 1 includes a sensor circuit box 11. The sensor circuit box 11 includes a top wall 12, a bottom wall 13 and a side wall 14. A sound pickup hole 15 is defined in the top wall 12, and the hole 15 is a through hole. The sound data collection device 1 also includes a sound sensor 16 arranged in the chamber. The sound sensor 16 is placed on the bottom wall 13 of the sensor circuit box through a bracket 17. At the opening of the sound pickup hole toward the chamber, above the sound sensor 16, a waterproof membrane 18 is provided. The waterproof membrane prevents water or moisture from entering the sound data collection device 1 and has good sound transmission performance.

[0129] Fig.12 The second embodiment of the sound data collection device 1 for the escalator monitoring system of the present invention is different from the first embodiment in that the sound sensor 16 is directly attached to the top wall 12 of the sensor circuit box 11, below the sound pickup hole 15 and also below the waterproof membrane 18.

[0130] Fig.13The third embodiment of the sound data collection device 1 of the escalator monitoring system used in the embodiments of the present invention. In this embodiment, the difference from the first embodiment is that the sound data collection device 1 is configured as a double-sided sound data collection device. Its sensor circuit box 11 has through-hole type sound pickup holes 15 and 19 in the top wall 12 and the bottom wall 13, respectively, and two sound sensors 16 and 20. The sound sensors are placed on the top wall 12 and the bottom wall 13 by brackets 17 and 21, respectively. Each sound pickup hole 15 and 19 is attached with a waterproof membrane 18 and 22 at the opening toward the chamber.

[0131] Fig.14 It is a fourth embodiment of the sound data collection device 1 of the escalator monitoring system used in various embodiments of the present invention. In this embodiment, the difference from the second embodiment is that the sound data collection device 1 is configured as a double-sided sound data collection device. Its sensor circuit box 11 has through-hole type sound pickup holes 15 and 19 in the top wall 12 and the bottom wall 13, respectively, and two sound sensors 16 and 20. Each sound sensor is directly attached to the top wall 12 and the bottom wall 13. Each sound pickup hole 15 and 19 is attached with a waterproof membrane 18 and 22 at the opening toward the chamber. Each waterproof membrane 18 and 22 is located between the opening of the sound pickup holes 15 and 19 toward the chamber and the sound sensor attached at the corresponding opening.

[0132] Below, refer to Figures 15 to 17 , a flowchart of a sixth embodiment of the escalator monitoring system according to the present invention is described in detail. This embodiment involves monitoring the temperature of the handrail of the escalator.

[0133] Fig.15The block diagram of the escalator monitoring system according to the present invention is shown. The monitoring system 100 includes a temperature sensor assembly 110, a data transmission unit 4 and a server 140. The temperature sensor assembly 110 is used to detect the ambient temperature and the handrail temperature when the escalator is in a shutdown state, a waiting state and a full-speed operation state. The back of the handrail is a surface that is in direct friction contact with the friction wheel, the bearing, the guide wheel or guide block of the handrail, and the metal of the curved arc section. For example, the temperature sensor assembly 110 includes an ambient temperature sensor for detecting the ambient temperature and a handrail temperature sensor for detecting the back temperature of the handrail. Generally speaking, when the escalator is in a shutdown state, the ambient temperature is equal to the handrail temperature. Therefore, for technicians who need to understand the operating state of the escalator, they are more concerned about the ambient temperature and the back temperature of the handrail when the escalator is in a waiting state and a full-speed operation state. In this embodiment, the temperature sensor assembly 110 detects the ambient temperature and the back temperature of the handrail when the escalator is in a full-speed operation state. The processor 120 collects the ambient temperature and the temperature of the back of the armrest detected by the temperature sensor assembly by wired or wireless means (such as radio frequency, Bluetooth, Zigbee), and transmits the ambient temperature and the temperature of the back of the armrest to the server 130 at a predetermined frequency (for example, once per minute).

[0134] The server 130 compares the ambient temperature with the temperature of the back of the armrest to obtain the temperature difference between the ambient temperature and the temperature of the back of the armrest, and a predetermined temperature difference threshold is stored in the server. If the temperature difference between the ambient temperature and the temperature of the back of the armrest exceeds the predetermined temperature difference threshold, the server sends an alarm signal.

[0135] Specifically, the server collects the temperature of the back of the armrest and the ambient temperature detected by the temperature sensor assembly 110 within a predetermined period t1, and compares the ambient temperature with the temperature of the back of the armrest to obtain a first average value M1 and a first variance value σ1 of the temperature difference between the ambient temperature and the temperature of the back of the armrest within the predetermined period. In addition, the server also collects the ambient temperature and the temperature of the back of the armrest detected by the temperature sensor assembly 110 within another predetermined period t2, and compares the ambient temperature with the temperature of the back of the armrest to obtain a second average value M2 and a second variance value σ2 of the temperature difference between the back of the armrest and the ambient temperature within the other predetermined period.

[0136] Next, the server compares the second average value with the first average value. If the difference between the second average value and the first average value is greater than k times the first variance value (M2>M1+kσ1), a first alarm signal is issued, where k is an integer greater than 1 set in the server.

[0137] Further, the server also determines the relationships between the first variance value σ1 and the first predetermined threshold T1, and between the second variance value σ2 and the second predetermined threshold T2. If the first variance value is less than the first predetermined threshold and the second variance value is less than the second predetermined threshold, i.e., σ1 < T1 and σ2 < T2, then the local data processing device or the cloud processor will send a second alarm signal. Compared with the first alarm signal, the second alarm signal is more accurate.

[0138] Fig.16 and Fig.17 respectively show the escalator in the friction wheel drive mode and the end wheel drive mode. Fig.16 Show the friction wheel 101 and the handrail 102. Fig.17 Show the end wheel 103. In these two different handrail drive modes, the running directions of the handrails are different. Since the installation position of the fixture for the temperature sensor assembly depends on the moving direction of the handrail, different situations will occur in the installation of the fixture in the two handrail drive modes, which will be introduced in detail below.

[0139] Figure 18-21 Show the fixture 104 installed on the C-shaped part of the skirt board in the friction wheel drive mode. As Figure 19-20 shown, the fixture 104 is installed near the friction wheel and is located on the C-shaped part of the skirt board. The fixture includes a bracket 41 and an adjustable plate 42. The adjustable plate 42 is installed on the bracket through a connecting device such as bolts and nuts. The bracket 41 is installed on the C-shaped part of the skirt board through bolts and nuts. The temperature sensor assembly 43 is installed at one end of the adjustable plate 42 away from the C-shaped part.

[0140] One of the adjustable plate 42 and the bracket 41 has a first longitudinal hole extending along the length direction, and the other of the adjustable plate and the bracket has an opening. Bolts can pass through the first longitudinal hole and the opening to connect the adjustable plate and the bracket, and according to the alignment position of the opening along the length direction with the first longitudinal hole, adjust the distance A between the temperature sensor assembly and the C-shaped part along the length direction. Thus, the handrail center distance L can be changed to adapt to different types of escalators.

[0141] Figure 22-25 Show the fixture 40 installed on the support column in the end wheel drive mode. As Fig. 22 and 23 shown, the fixture is installed on the support column. The fixture 40 includes a bracket 41 and an adjustable plate 42. The adjustable plate 42 is installed on the bracket through a connecting device such as bolts and nuts. The bracket 41 is installed on the support column through bolts and nuts. The temperature sensor assembly 43 is installed at one end of the adjustable plate 42 away from the support column.

[0142] One of the adjustable plate and the bracket has a second longitudinal hole 44 extending in the width direction perpendicular to the length direction, and the other of the adjustable plate and the bracket has a third longitudinal hole 45 extending in the length direction. Bolts can pass through the second longitudinal hole and the third longitudinal hole to connect the adjustable plate and the bracket. They also adjust the distance between the temperature sensor assembly and the support in the length direction (the center distance L of the handrail) and the distance W between the temperature sensor assembly and the handrail in the width direction according to the alignment position of the second longitudinal hole and the third longitudinal hole.

[0143] Fig.26 A flow chart of a first embodiment of a monitoring method for an escalator according to the present invention is shown. In step 100', a temperature sensor assembly is used to detect the ambient temperature and the temperature of the handrail when the escalator is in a stopped state, a standby operation state, and a full-speed operation state. In step 200', a data transmission unit is used to receive the ambient temperature and the temperature of the back of the handrail from the temperature sensor assembly, and transmit the ambient temperature and the temperature of the back of the handrail to a local data processing device or a cloud processor at a predetermined frequency. In step 300', the server compares the ambient temperature with the temperature of the back of the handrail to obtain the temperature difference between the ambient temperature and the temperature of the back of the handrail, and a predetermined temperature difference threshold is stored in the local data processing device or the cloud processor. If the temperature difference between the ambient temperature and the temperature of the back of the handrail exceeds the predetermined temperature difference threshold, the server will send an alarm signal.

[0144] In step 300', the server also collects the armrest back temperature and the ambient temperature detected by the temperature sensor component within a predetermined time period, and compares the ambient temperature with the armrest back temperature to obtain a first average value and a first variance value of the temperature difference between the ambient temperature and the armrest back temperature within the predetermined time period.

[0145] In step 300', the server also collects the ambient temperature and the temperature of the back of the armrest detected by the temperature sensor component within another predetermined time period, and compares the ambient temperature with the temperature of the back of the armrest to obtain a second average value and a second variance value of the temperature difference between the temperature of the back of the armrest and the ambient temperature within the other predetermined time period.

[0146] In step 300', the server is configured to issue a first alarm signal when the difference between the second average value and the first average value is greater than k times the first variance value, wherein k is an integer greater than 1 set in the server.

[0147] In step 300', the server is further configured to determine the relationship between the first variance value and a predetermined first threshold value, and the relationship between the second variance value and a predetermined second threshold value. If the first variance value is less than the predetermined first threshold value, and the second variance value is less than the predetermined second threshold value, the server issues a second alarm signal.

[0148] The escalator monitoring system can monitor the operation status of the handrail in real time. Possible problems can be known in advance, thus providing a safer escalator. In addition, the real-time monitoring device is suitable for different installation conditions of escalators, and the fixture of the temperature sensor assembly is adjustable. The installation position of the fixture can be adjusted according to the different installation sizes of various types of escalators.

[0149] Below, refer to Figures 27 to 31 , a flowchart of an eighth embodiment of the escalator monitoring system according to the present invention is described in detail. This embodiment relates to the monitoring of passenger flow by the escalator monitoring system according to the present invention.

[0150] Fig. 27 A block diagram of an escalator monitoring system according to the present invention is shown. The monitoring system 100 includes a passenger flow sensor 211, a motor power sensor 212, a data sending unit 4 and a server 130. The passenger flow sensor 211 is a device installed at the entrance of the escalator, and is used to emit light beams in opposite directions. The frequency at which the light beams are interrupted reflects the passenger flow entering the escalator. The motor power sensor 212 is configured to detect the motor power consumed per unit time. The data sending unit 4 collects the passenger flow and motor power per unit time, and transmits them to the server 130 via, for example, 2G, 3G or 4G. The server 130 calculates the ratio (KPI) of the passenger flow per unit time to the motor power per unit time, and compares the ratio with a predetermined ratio stored in the server 130. If the ratio KPI has an abnormal mutation relative to the predetermined ratio, the server predicts the time when the escalator fails and sends a predictive maintenance signal. For example, Fig.28 The number "2" in the figure shows that the ratio of passenger flow per unit time to motor power per unit time suddenly increases, which indicates that the passenger flow at this time is not matched with the motor power consumption, and the escalator fails. Those skilled in the art should understand that the passenger flow per unit time is the ratio of the total passenger flow in a certain period of time to the time, and the total passenger flow in the period of time cannot be too small.

[0151] Fig.29 and Fig.30 Two mismatched relationships between passenger flow per unit time and motor power consumption per unit time are shown respectively. Fig.29 It is shown that if the passenger flow sensor detects no passenger flow, and the motor power consumption detected by the motor power sensor does not match the predetermined power consumption in the absence of passenger flow, the escalator is considered to be faulty and the server sends an alarm signal. Fig.30 It is shown that during a period exceeding a predetermined threshold, if the traffic sensor detects at least some traffic while the motor power consumption is running in a standby state, the escalator suddenly accelerates, causing passengers to fall and get injured, and the server sends an alarm signal.

[0152] In addition, when there is no passenger flow, the power sensor detects the average power consumption within a predetermined period of time. A predetermined threshold range is stored in the server. If the average power consumption does not fall within the predetermined threshold range stored in the server, the server generates an alarm signal.

[0153] Fig.31 A flow chart showing a second embodiment of the escalator monitoring method according to the present invention.

[0154] In step 2100, a passenger flow sensor is used to detect the passenger flow entering the elevator per unit time. In step 2200, a power sensor is used to detect the motor power consumption per unit time. In step 2300, a data transmission unit is used to transmit the detected passenger flow per unit time and the detected motor power consumption per unit time to a server. In step 2400, the server determines the operating state of the escalator based on the relationship between the passenger flow per unit time and the power consumption per unit time to determine whether to send an alarm signal.

[0155] In addition, in step 2400, the server also calculates the ratio of power consumption per unit time to passenger flow per unit time. If the ratio has an abnormal mutation, an alarm signal is issued; if the ratio has no abnormal mutation but has an increasing trend within a predetermined time and exceeds a predetermined threshold, the server issues an alarm signal.

[0156] In addition, when the escalator is in standby operation, in step 2200, the power sensor detects the average power within a predetermined period of time. In step 2400, if the average power does not fall within the predetermined threshold range stored in the server, the server sends an alarm signal.

[0157] In step 2400, the server is further configured to send an alarm signal if the passenger flow detected by the passenger flow sensor is zero and the power consumption detected by the power sensor does not match the power consumption set for zero passenger flow during a period exceeding a predetermined threshold period.

[0158] In step 2400, the server is further configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is standby power output during a period exceeding a predetermined threshold period.

[0159] By means of the real-time monitoring device of the present invention, the relationship between passenger flow and power output can be monitored in real time, and the operating status of the escalator can be understood, thereby improving the maintenance of the escalator, improving the energy consumption of the escalator, and providing the safety of the escalator.

[0160] The server described herein can be a local processor together with a data collection device, or a cloud processor capable of remote monitoring. All data processing can be processed only at the local processor, or only at the cloud processor, or as described above in relation to the embodiments, partially at the local processor and then the rest at the cloud processor. The present invention is herein combined with various embodiments based on these concepts and is not limited to those embodiments described above.

[0161] The above embodiments are exemplary only and should not be considered as limiting the present invention. The features in the various embodiments may be combined to implement more embodiments of the present invention, the scope of which is determined only by the appended claims. Various modifications and changes may be made to the described embodiments without departing from the scope of the present invention.

Claims

1. An escalator monitoring system, comprising a data collection device, arranged near a component of the escalator that needs to be monitored, for collecting data of the component; a data sending device, for sending data related to the safe operation of the escalator; a cloud processor, for receiving the data related to the safe operation of the escalator, comparing it with a threshold value stored in the cloud processor and obtained from the component under normal operating conditions, and responding based on the comparison result, in, The data collection device includes a passenger flow sensor and a power sensor. The passenger flow sensor is used to detect the passenger flow entering the escalator per unit time, and the power sensor is used to detect the power consumption per unit time. The data sending unit sends the detected passenger flow per unit time and the detected power consumption per unit time to a local data processing device or a cloud processor. The local data processing device or the cloud processor determines the operating status of the escalator based on the relationship between the passenger flow per unit time and the power consumption per unit time to determine whether an alarm signal needs to be issued.

2. The escalator monitoring system according to claim 1, wherein: The local data processing device or cloud processor calculates the ratio of power consumption per unit time to passenger flow per unit time. If the ratio has an abnormal mutation, the local data processing device or cloud processor will issue an alarm signal; if the ratio has no abnormal mutation but has an increasing trend within a predetermined time and is greater than a predetermined threshold, the local data processing device or cloud processor will issue an alarm signal.

3. The escalator monitoring system according to claim 2, wherein: When the escalator is running at idle speed, the power sensor detects the average power within a predetermined period of time. If the average power does not fall within a predetermined threshold range stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor will issue an alarm signal.

4. The escalator monitoring system according to claim 1, wherein: The local data processing device or cloud processor is also configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the passenger flow is zero within a time period exceeding a predetermined threshold.

5. The escalator monitoring system according to claim 1, wherein: The local data processing device or cloud processor is also configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is idle power output within a period exceeding a predetermined threshold.

6. The escalator monitoring system according to claim 1, wherein: The escalator monitoring system further comprises a local data processing device, and the data related to the safe operation of the escalator is the data processed by the local data processing device.

7. The escalator monitoring system according to claim 6, wherein: The local data processing device calculates characteristic values ​​of the data as data related to the safe operation of the escalator.

8. The escalator monitoring system according to claim 7, wherein: If the comparison result is greater than the threshold value, the local data processing device sends an alarm signal regarding the safe operation of the escalator, or sends the comparison result to a cloud processor.

9. The escalator monitoring system according to claim 1, wherein: The data related to the safe operation of the escalator are data collected by the data collection device, which are received and processed by the cloud processor, and then compared with threshold values ​​stored in the cloud processor and obtained from components under normal operating conditions.

10. The escalator monitoring system according to claim 9, wherein: The cloud processor calculates feature values ​​of the data as data related to the safe operation of the escalator, and then compares it with threshold values ​​stored in the cloud processor and obtained from the components under normal operating conditions.

11. The escalator monitoring system according to claim 10, wherein: If the comparison result is greater than the threshold, the cloud processor sends out an alarm signal regarding the safe operation of the escalator, and triggers the customer service system or directly communicates with the maintenance personnel to perform lubrication maintenance.

12. The escalator monitoring system according to claim 1, wherein: The cloud processor includes an escalator database, which stores escalator operation history data, escalator parameters and escalator maintenance history.

13. The escalator monitoring system according to claim 1, wherein: The cloud processor compares the processed data and thresholds through statistical, analytical, artificial intelligence and machine learning methods.

14. The escalator monitoring system according to claim 1, wherein: The cloud processor stores the component’s data related to the safe operation of the escalator.

15. The escalator monitoring system according to claim 1, wherein: If the comparison result is greater than the threshold, the cloud processor sends an alarm signal about the safety status of the escalator and triggers the customer service system.

16. The escalator monitoring system according to claim 1, wherein: The local data processing device is a microcomputer or a DSP chip.

17. The escalator monitoring system according to claim 16, wherein: The microcomputer transmits data to the cloud processor via a wireless network.

18. An escalator, comprising the escalator monitoring system according to any one of the preceding claims 1-17.

19. A method for monitoring an escalator, comprising the following steps: A passenger flow sensor is used to detect the passenger flow entering the escalator per unit time; Use power sensors to detect power consumption per unit time; The data transmission unit transmits the detected passenger flow within a unit time and the detected power consumption within a unit time to a local data processing device or a cloud processor; The server determines the running state of the escalator according to the relationship between the passenger flow rate per unit time and the power consumption per unit time to determine whether an alarm signal needs to be issued.

20. The method according to claim 19, wherein: The local data processing device or cloud processor calculates the ratio of power consumption per unit time to passenger flow per unit time. If the ratio has an abnormal mutation, the local data processing device or cloud processor will issue an alarm signal; if the ratio has no abnormal mutation but has an increasing trend within a predetermined time and is greater than a predetermined threshold, the local data processing device or cloud processor will issue an alarm signal.

21. The method according to claim 20, wherein: When the escalator is running at idle speed, the power sensor detects the average power within a predetermined period of time. If the average power does not fall within a predetermined threshold range stored in the local data processing device or the cloud processor, the local data processing device or the cloud processor will issue an alarm signal.

22. The method according to claim 21, wherein: The local data processing device or cloud processor is also configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is zero and the power consumption detected by the power sensor does not match the set power consumption when the passenger flow is zero within a time period exceeding a predetermined threshold.

23. The method according to claim 21, wherein: The local data processing device or cloud processor is also configured to send out an alarm signal if the passenger flow detected by the passenger flow sensor is not zero and the power consumption detected by the power sensor is idle power output within a period exceeding a predetermined threshold.

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