Elevator fault remote monitoring system based on Internet of Things

Abstract

The invention relates to the technical field of elevators, and discloses an elevator fault remote monitoring system based on the Internet of Things, which is characterized in that a temperature sensor is arranged in an elevator machine room and is used for detecting the temperature in the elevator machine room; the vibration sensor is used for collecting elevator vibration signals; the accelerometer is used for reading vibration parameters; the microcontroller is used for explaining a signal from the sensor and controlling the actuator; the microcontroller performs network setting by using Wi-Fi (Wireless Fidelity); in the process of collecting the elevator data, the data are preprocessed based on an Internet of Things LSTM algorithm, and data missing and redundancy are reduced; the method comprises the steps that firstly, the temperature of an elevator machine room and vibration of an elevator car are monitored, logic operation of read values is executed according to the preset severity condition level of a system by means of the intelligent capacity of a microcontroller, and finally, the system is informed of abnormality through Wi-Fi by using a Web application program, and fault data obtained after operation are sent to the cloud end.

Description

Technical Field

[0001] The present invention relates to the field of elevators, and more specifically, to an elevator fault remote monitoring system based on the Internet of Things. Background Art

[0002] Since elevators work frequently and the elevator system is unattended, they need to be remotely monitored and regularly maintained; however, the interaction between the remote tracking and maintenance system in the prior art and its use environment is static, and it is impossible to monitor the remote status of the elevator in real time.

[0003] Therefore, the present invention provides an elevator fault remote monitoring system based on the Internet of Things, which improves the above-mentioned technical problems. Summary of the invention

[0004] The disclosed embodiments aim to address the deficiencies of the prior art and provide an elevator fault remote monitoring system based on the Internet of Things. The present invention pre-processes the temperature data and vibration signals collected by the sensor based on the Internet of Things, and remotely monitors the processed vibration data and temperature data through a remote status monitoring system.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions: an elevator fault remote monitoring system based on the Internet of Things, comprising: a temperature sensor, a vibration sensor, an accelerometer, a microcontroller, an LED, a power supply and a jumper;

[0006] The temperature sensor is installed in the elevator machine room and is used to detect the temperature in the elevator machine room;

[0007] The vibration sensor is connected to the female-to-female and male-to-male jumper wires, and the vibration sensor is used to collect the elevator vibration signal;

[0008] The accelerometer is used to read vibration parameters, and the acceleration is used in conjunction with an analog-to-digital converter module of a microcontroller;

[0009] The microcontroller is used to interpret signals from sensors and control actuators; the microcontroller uses Wi-Fi for network settings;

[0010] The LED encodes the microcontroller's interpretation of the sensor's reading;

[0011] The power supply is used for supplying power.

[0012] As a preferred technical solution of the present invention, the temperature sensor and the vibration sensor collect temperature data and vibration data of the elevator; there will be missing and redundant data in the collection process, and the data is preprocessed based on the LSTM algorithm.

[0013] As a preferred technical solution of the present invention, the process of preprocessing data based on the LSTM algorithm is:

[0014] Selectively forget the input of the previous node and use the activation function to obtain the output of the forget gate f t ;

[0015] The calculation formula of the forget gate is:

[0016] f t =σ(W f ·[h t-1 ,x1]+b f ) where f t : Forget gate output, σ: Sigmoid activation function, w f : Forget gate weight matrix, b f : Forget gate bias, h t-1 : The hidden state of the previous time step, x t : Input of the current time step;

[0017] Selectively memorize the input, write down meaningful content, and omit less critical information; the input gate has two components: the output of the first part using the sigmoid activation function is i(t); the result of the second part using the tanh activation function is

[0018] The calculation formula of the input gate is:

[0019] i(t)=σ(W i ·[h t-1 , x t ]+b i )

[0020] Among them, i t : Input gate output; w i : Input gate weight matrix; b i : Input gate bias term;

[0021]

[0022] in, Candidate memory cell state; tanh: hyperbolic tangent activation function; w c : Candidate memory cell weight matrix; b c : Candidate memory cell bias;

[0023] The results of the previous forget gate and input gate will affect the cell state C t ; Update unit status C t The calculation formula is:

[0024]

[0025] Among them, C t : The memory cell state at the current time step; C t-1 : The memory cell state of the previous time step; f t 、i t and The states from the forget gate, input gate and candidate memory cells respectively;

[0026] Decide which output to consider for the current condition; first, run a sigmoid layer to determine which part of the cell state will be output; then, process the cell state through the tanh function and multiply it by the output of the sigmoid gate;

[0027] The calculation formula of the output gate is:

[0028] o t =σ(W o [h t-1 ,x t ]+b o )

[0029] Among them, t : Output gate output; w o : Output gate weight matrix; b o : Output gate bias term;

[0030] h t =o t *tanh(C t );

[0031] Among them, h t : The hidden state of the current time step; tanh(C t ): Perform nonlinear transformation on the current memory cell state through the hyperbolic tangent activation function.

[0032] As a preferred technical solution of the present invention, since the room temperature in the elevator machine room is a variable, it is assumed that: the temperature variable of the read signal is T; the output voltage of the temperature sensor is t;

[0033] The temperature data is calculated as:

[0034] T(°C)=(500*t) / 1024.

[0035] As a preferred technical solution of the present invention, the output value of the accelerometer can be calibrated to a value in meters per second or gravity; the conversion is completed using the following equation:

[0036]

[0037] Among them, AdcR xis the signal value of the sensor; V ref is the reference voltage, which is 5V; V Zero G is the voltage at zero gravity; n is the number of bits of the sensor, and S is the sensitivity of the sensor.

[0038] As a preferred technical solution of the present invention, since the three-axis vibration of the elevator car is a variable, the average vibration signal on the X, Y, and Z axes is

[0039] The calculation formula of the vibration signal is:

[0040]

[0041] in, The vibration range under normal working conditions is ±5mg:

[0042]

[0043] As a preferred technical solution of the present invention, when it exceeds ±5mg, the microcontroller sends data information and fault notification through coding, and the process is:

[0044] if:

[0045] or

[0046] or

[0047] Or T℃>35℃

[0048] Then: Use the web application via Wi-Fi to send a notification "System failure" and send data to the cloud;

[0049] Otherwise: the system is normal, end.

[0050] In summary, the present invention has the following beneficial effects: in the process of collecting elevator data, the data is preprocessed based on the Internet of Things LSTM algorithm to reduce data loss and redundancy; then, by monitoring the temperature of the elevator machine room and the vibration of the elevator car, the intelligent capabilities of the microcontroller are used to perform logical operations on the read values ​​according to the system's predetermined severity condition level, and finally, the system is notified of abnormalities using a Web application via Wi-Fi and the calculated fault data is sent to the cloud. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A framework diagram of an elevator fault remote monitoring system based on the Internet of Things provided by an embodiment of the present invention; DETAILED DESCRIPTION

[0052] The present application is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can also be made without departing from the concept of the present application. These all belong to the protection scope of the present application.

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional module division is performed in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a sequence different from the module division in the device or the flow chart. In addition, the words "first", "second", "third", etc. used herein do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0055] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in this specification and in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0056] In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0057] The disclosed embodiment aims to solve the problem that the interaction between the remote tracking maintenance system and its use environment in the prior art is static and cannot monitor the remote status of the elevator in real time. In view of this, the disclosed embodiment proposes an elevator fault remote monitoring system based on the Internet of Things, which pre-processes the temperature data and vibration signals collected by the sensor based on the Internet of Things, and uses a remote status monitoring system to remotely monitor the processed vibration data and temperature data.

[0058] Please refer to Figure 1 , Figure 1 The framework diagram of the elevator fault remote monitoring system based on the Internet of Things according to the embodiment of the present disclosure is shown; it mainly consists of the following parts: a temperature sensor, a vibration sensor, an accelerometer, a microcontroller, an LED, a power supply and a jumper;

[0059] The temperature sensor is installed in the elevator machine room and is used to detect the temperature in the elevator machine room;

[0060] The vibration sensor is connected to the female-to-female and male-to-male jumper wires, and the vibration sensor is used to collect the elevator vibration signal;

[0061] The accelerometer is used to read vibration parameters, and the acceleration is used in conjunction with an analog-to-digital converter module of a microcontroller;

[0062] The microcontroller is used to interpret signals from sensors and control actuators; the microcontroller uses Wi-Fi for network settings;

[0063] The LED encodes the microcontroller's interpretation of the sensor's reading;

[0064] The power supply is used for supplying power.

[0065] Specifically, the temperature and vibration data of the elevator are collected through temperature sensors and vibration sensors. There will be missing and redundant data in the collection process. The data is preprocessed based on the LSTM algorithm. The specific process is as follows:

[0066] Selectively forget the input of the previous node and use the activation function to obtain the output of the forget gate f t .

[0067] The calculation formula of the forget gate is:

[0068] f t =σ(W f ·[h t-1 ,x1]+b f )

[0069] Among them, f t : Forget gate output, σ: Sigmoid activation function, w f : Forget gate weight matrix, b f : Forget gate bias, h t-1 : The hidden state of the previous time step, x t : Input of the current time step;

[0070] Selectively memorize the input, write down meaningful content, and omit less critical information; the input gate has two components: the output of the first part using the sigmoid activation function is i(t); the result of the second part using the tanh activation function is

[0071] The calculation formula of the input gate is:

[0072] i(t)=σ(W i ·[ht-1 , x t ]+b i )

[0073] Among them, i t : Input gate output; w i : Input gate weight matrix; b i : Input gate bias term;

[0074]

[0075] in, Candidate memory cell state; tanh: hyperbolic tangent activation function; w c : Candidate memory cell weight matrix; b c : Candidate memory cell bias;

[0076] The results of the previous forget gate and input gate will affect the cell state C t ; Update unit status C t The calculation formula is:

[0077]

[0078] Among them, C t : The memory cell state at the current time step; C t-1 : The memory cell state of the previous time step; f t 、i t and The states from the forget gate, input gate and candidate memory cells respectively;

[0079] Decide which output to consider for the current condition; first, run a sigmoid layer to determine which part of the cell state will be output; then, process the cell state through the tanh function and multiply it by the output of the sigmoid gate;

[0080] The calculation formula of the output gate is:

[0081] o t =σ(W o [h t-1 , x t ]+b o )

[0082] Among them, t : Output gate output; w o : Output gate weight matrix; b o : Output gate bias term;

[0083] h t =o t *tanh(C t );

[0084] Among them, h t : The hidden state of the current time step; tanh(C t ): Perform nonlinear transformation on the current memory cell state through the hyperbolic tangent activation function.

[0085] Analyze the preprocessed monitoring data.

[0086] Specifically, since the room temperature is a variable, let: the temperature variable of the read signal be T; the output voltage of the temperature sensor be t;

[0087] The temperature data is calculated as:

[0088] T(℃)=(500*t) / 1024

[0089] Specifically, the output of the accelerometer can be calibrated to a value in meters per second or gravity; the conversion is done using the following equation;

[0090]

[0091] Among them, AdcR x is the signal value of the sensor; V ref is the reference voltage, which is 5V; V Zero G is the voltage at zero gravity; n is the number of bits of the sensor, S is the sensitivity of the sensor;

[0092] Since the three-axis vibration of the elevator car is a variable, the average vibration signal on the X, Y, and Z axes is

[0093] The calculation formula of the vibration signal is:

[0094]

[0095] in, The vibration range under normal working conditions is ±5mg:

[0096]

[0097] When it exceeds ±5mg, the microcontroller sends data information and fault notification through coding. The specific process is as follows:

[0098] if:

[0099] or

[0100] or

[0101] or T℃>35℃

[0102] Then: Use the web application via Wi-Fi to send a notification "System failure" and send data to the cloud;

[0103] Otherwise: the system is normal, end.

[0104] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. The elevator fault remote monitoring system based on the Internet of Things is characterized by: include: Temperature sensor, vibration sensor, accelerometer, microcontroller, LEDs, power supply and jumper wires; The temperature sensor is installed in the elevator machine room and is used to detect the temperature in the elevator machine room; The vibration sensor is connected to the female-to-female and male-to-male jumper wires, and the vibration sensor is used to collect the elevator vibration signal; The accelerometer is used to read vibration parameters, and the acceleration is used in conjunction with an analog-to-digital converter module of a microcontroller; The microcontroller is used to interpret signals from sensors and control actuators; the microcontroller uses Wi-Fi for network settings; The LED encodes the microcontroller's interpretation of the sensor's reading; The power supply is used for supplying power.

2. The elevator fault remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The temperature sensor and vibration sensor collect temperature data and vibration data of the elevator; there will be missing and redundant data in the collection process, and the data is preprocessed based on the LSTM algorithm.

3. The elevator fault remote monitoring system based on the Internet of Things according to claim 2 is characterized in that: The process of preprocessing data based on the LSTM algorithm is as follows: Selectively forget the input of the previous node and use the activation function to obtain the output of the forget gate f t ; The calculation formula of the forget gate is: f t =σ(W t ·[h t-1 ,x1]+b f ) Among them, f t : Forget gate output, σ: Sigmoid activation function, w f : Forget gate weight matrix, b f : Forget gate bias, h t-1 : The hidden state of the previous time step, x t : Input of the current time step; Selectively memorize the input, write down meaningful content, and omit less critical information; the input gate has two components: the output of the first part using the sigmoid activation function is i(t); the result of the second part using the tanh activation function is The calculation formula of the input gate is: i(t)=σ(W i ·[h t-1 ,x t ]+b i ) Among them, i t : Input gate output; w i : Input gate weight matrix; b i : Input gate bias term; in, Candidate memory cell status; t anh : Hyperbolic tangent activation function; W c : Candidate memory cell weight matrix; b c : Candidate memory cell bias; The results of the previous forget gate and input gate will affect the cell state C t ; Update unit status C t The calculation formula is: Among them, C t : The memory cell state at the current time step; C t-1 : The memory cell state of the previous time step; f t 、i t and The states from the forget gate, input gate and candidate memory cells respectively; Decide which output to consider for the current condition; first, run a sigmoid layer to determine which part of the cell state will be output; then, process the cell state through the tanh function and multiply it by the output of the sigmoid gate; The calculation formula of the output gate is: the t =σ(W o [h t-1 ,x t ]+b o ) Among them, t : Output gate output; w o : Output gate weight matrix; b o : Output gate bias term; h t =o t *tanh(C t ); Among them, h t : The hidden state of the current time step; tanh(C t ): Perform nonlinear transformation on the current memory cell state through the hyperbolic tangent activation function.

4. The elevator fault remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: Since the room temperature in the elevator room is a variable, let: the temperature variable of the read signal be T; the output voltage of the temperature sensor be t; The temperature data is calculated as: T(°C)=(500*t) / 1024.

5. The elevator fault remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The output of the accelerometer can be calibrated to values ​​in meters per second or in gravity; The conversion is done using the following equation; Among them, AdcR x is the signal value of the sensor; V ref is the reference voltage, which is 5V; V zero G is the voltage at zero gravity; n is the number of bits of the sensor, and S is the sensitivity of the sensor.

6. The elevator fault remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: Since the three-axis vibration of the elevator car is a variable, the average vibration signal on the X, Y, and Z axes is The calculation formula of the vibration signal is: in, The vibration range under normal working conditions is ±5mg:

7. The elevator fault remote monitoring system based on the Internet of Things according to claim 6 is characterized in that: When it exceeds ±5mg, the microcontroller sends data information and fault notification through coding. The process is as follows: if: or or Or T℃>35℃ Then: Use the web application via Wi-Fi to send a notification "System failure" and send data to the cloud; Otherwise: the system is normal, end.

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