Elevator fault detection and alarm system and method

Abstract

The invention discloses an elevator fault detection and alarm system and method, and relates to the technical field of elevator fault detection, the elevator fault detection and alarm system comprises an elevator data acquisition module, an elevator load and buffer state detection module and an elevator load and buffer state analysis module; the elevator data collecting module monitors deformation of a steel plate spring and displacement of a measuring element in a linear displacement sensor through a load sensor and the linear displacement sensor, and accurately collects elevator load and buffer compression amount data. The elevator load and buffer state detection module analyzes the collected data, learns a normal operation mode of an elevator, evaluates the state of a buffer, and constructs a detection model based on a genetic wavelet neural network algorithm; and the elevator load and buffer state analysis module quickly judges whether the elevator is overloaded or not and whether the buffer works normally or not according to model output, so that safe operation of the elevator is ensured, the fault risk is reduced, and real-time and intelligent fault early warning is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator fault detection, and specifically to an elevator fault detection and alarm system and method. Background Art

[0002] With the acceleration of the urbanization process and the continuous increase of high-rise buildings, elevators, as the main means of vertical transportation, their safety and reliability have attracted more and more attention. Traditional elevator fault detection and alarm methods often rely on manual inspections and passive responses after faults. This method is not only inefficient but also difficult to prevent accidents. Therefore, the application of elevator fault detection and alarm systems has become particularly important. At present, significant progress has been made in elevator fault detection and alarm systems. By applying advanced technologies such as the Internet of Things, cloud computing, and artificial intelligence, the system can achieve real-time monitoring of elevator status, data collection, and fault early warning. Once a fault occurs in the elevator, the system can immediately notify the maintenance personnel and provide accurate fault information, thereby shortening the fault response time and improving the maintenance efficiency. In addition, with the development of intelligent automation technology, elevator fault detection and alarm systems can also achieve remote troubleshooting and preventive maintenance. Through pre-installed advanced instrument equipment, the system completes the remote and rapid detection and troubleshooting, timely discovers potential fault hazards in the elevator, and processes them in advance.

[0003] Although the current elevator fault detection and alarm systems have been widely applied, they still face safety hazards caused by delayed overload display and damaged buffers. Delayed overload display may cause the elevator to operate overloaded, increasing the risk of accidents; while damaged buffers may not provide effective buffering when the elevator falls, endangering the lives of passengers. To solve the above problems, by collecting and analyzing elevator load data and buffer status data, an elevator load and buffer status detection model based on the genetic wavelet neural network algorithm is established to deeply learn the normal operation mode of the elevator to accurately judge the operating state of the elevator. This model can process a large amount of data in real time, quickly identify the overload situation of the elevator and the health status of the buffer, thereby issuing early warnings in a timely manner and effectively avoiding potential safety risks. This innovative method not only improves the accuracy and efficiency of elevator fault detection but also provides a strong guarantee for the safe operation of the elevator, allowing passengers to feel more at ease while enjoying the convenience. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides an elevator fault detection and alarm system, which solves the problems in the above background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: An elevator fault detection and alarm system includes an elevator data collection module, an elevator load and buffer status detection module, and an elevator load and buffer status analysis module; The elevator data acquisition module includes a load sensor and a linear displacement sensor, which collect elevator load data and buffer compression amount data by detecting the displacement generated by the deformation of the leaf spring and the internal measuring element of the linear displacement sensor. The elevator load and buffer state detection module analyzes the elevator load data and buffer compression amount data, learns the normal operation mode of the elevator, judges the buffer state, and establishes an elevator load and buffer state detection model based on the genetic wavelet neural network algorithm. The elevator load and buffer state analysis module judges whether the elevator is overloaded and whether the buffer is in a normal working state based on the output results of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm.

[0006] Further, the process of the elevator data acquisition module collecting elevator load data through the load sensor includes: The load sensor is installed at the bottom of the elevator car. When the elevator bears weight, the load sensor detects the deformation of the leaf spring caused by the weight change. The deformation then generates a displacement amount. The load sensor measures this displacement amount and converts it into an output of voltage change through an internal circuit. This voltage change is proportional to the elevator load. Therefore, the elevator load is calculated by measuring the voltage change. The elevator data acquisition module is responsible for receiving the electrical signal converted by the load sensor and further processing it into a digital signal.

[0007] Further, the process of the elevator data acquisition module monitoring the buffer compression amount through the linear displacement sensor includes: The linear displacement sensor monitors the buffer compression amount data based on the principle of precise perception and conversion of displacement by its internal measuring element. The internal measuring element of it undergoes corresponding displacement changes as the buffer is compressed. This displacement change is captured by the circuit inside the sensor and converted into an electrical signal. This electrical signal is proportional to the buffer compression amount. The elevator data acquisition module is responsible for receiving the electrical signal converted by the linear displacement sensor and further processing it into a digital signal.

[0008] Further, the process of the elevator load and buffer state detection module training the input layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm by analyzing the elevator load data and buffer compression amount data includes: Clean and sort the collected elevator load data and buffer compression amount data, remove invalid data and outliers, normalize the data, and establish a genetic wavelet neural network model. The genetic wavelet neural network model includes an input layer, a hidden layer, and an output layer. The input layer is responsible for receiving the elevator load data and buffer compression amount data and converting the continuous load data and buffer compression amount data into vector representations.

[0009] Furthermore, the process of training the hidden layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm by the elevator load and buffer state detection module through non-linear transformation and feature extraction of the input data includes: The hidden layer of the genetic wavelet neural network model uses the Morlet wavelet function as the activation function, takes the wavelet coefficients obtained through wavelet analysis as the input of the hidden layer, captures the high-frequency and low-frequency components in the collected data, performs multi-scale analysis on the input data, and thus extracts the features that are discriminative for the elevator operation state and buffer state. Then, the hidden layer uses the genetic algorithm to optimize the weights and thresholds of the model, randomly generates a set of combinations of neural network weights and thresholds as the initial population, configures the weights and thresholds of each individual to the neural network, and inputs the elevator load data and buffer compression data for training. According to the error between the fault detection result and the actual fault situation, calculates the fitness of each individual. According to the fitness value, selects the individuals with high fitness for replication, retains their excellent features in the next generation, performs selection and replication operations through the tournament selection method, partially exchanges the weights and thresholds of two individuals to generate new offspring, randomly changes the values of some weights and thresholds to increase the diversity of the population, repeats the above genetic algorithm steps, continuously iteratively optimizes the weights and thresholds of the neural network. When the iteration process ends, outputs the individual with the highest fitness as the optimal solution to obtain the optimal configuration of neural network weights and thresholds. In addition, through the interaction of each neuron in the hidden layer, fuses the extracted feature information to form a higher-level feature representation and outputs it to the output layer for the final judgment.

[0010] Furthermore, the process of the elevator load and buffer state detection module learning the normal operation mode of the elevator, judging the buffer state, and training the output layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm includes: The output layer of the genetic wavelet neural network model receives the output data of the hidden layer and makes a fault judgment according to the preset judgment rules. If the elevator load exceeds the preset elevator load threshold, it is judged that the elevator is overloaded. If the elevator load does not exceed the preset elevator load threshold, it is judged that the elevator load is normal. If the buffer compression exceeds the compression threshold, it is judged that the buffer is damaged. If the buffer compression is lower than the compression threshold, it is judged that the buffer state is normal.

[0011] Furthermore, the process of the elevator load and buffer state analysis module judging whether the elevator is overloaded and whether the buffer is in a normal working state based on the output result of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm includes: For large passenger elevators, the maximum load is set at 1600 kg. When the actual load is detected to exceed 1600 kg, the system determines that it is overloaded. An overload prompt is given on the elevator display screen to prevent the elevator from closing and starting, ensuring the safety of passengers in case of overload. When the actual load is detected to be lower than 1600 kg, the system determines that the load is normal and the elevator operates normally; The compression stroke of the elevator buffer is set between 200 and 250 mm. When the system detects that the compression amount of the buffer exceeds 250 mm, the system determines that the buffer status is abnormal and sends the buffer fault information to the elevator maintenance personnel for timely repair and handling. When the system detects that the compression amount of the buffer is within 250 mm, the system determines that the buffer status is normal and the elevator operates normally.

[0012] Furthermore, an elevator fault detection and alarm method includes the following steps: S1. Respectively detect the displacement amounts generated by the deformation of the leaf spring and the internal measuring element of the linear displacement sensor through the load sensor and the linear displacement sensor, and collect the elevator load data and the buffer compression amount data; S2. Analyze the elevator load data and the buffer compression amount data, and establish an elevator load and buffer status detection model based on the genetic wavelet neural network algorithm; S3. Based on the output results of the elevator load and buffer status detection model based on the genetic wavelet neural network algorithm, judge whether the elevator is overloaded and whether the buffer is in a normal working state.

[0013] The present invention provides an elevator fault detection and alarm system and method. It has the following beneficial effects: First of all, through the accurate data collection of the load sensor and the linear displacement sensor, the system can obtain accurate information on the elevator load and the buffer compression amount in real time, providing a reliable data basis for subsequent fault detection. Secondly, the elevator load and buffer status detection module uses the genetic wavelet neural network algorithm to deeply analyze the collected data, learn the normal operation mode of the elevator, and establish a detection model based on this. This model not only improves the accuracy of fault detection, but also can adapt to various changes during the operation of the elevator, enhancing the adaptability and robustness of the system. Finally, according to the output results of the detection model, the elevator load and buffer status analysis module can quickly judge whether the elevator is overloaded and whether the buffer is in a normal working state. Once an abnormal situation is found, the system can immediately send an alarm signal and take timely measures to avoid potential safety risks, effectively ensuring the safety of passengers' lives and the normal operation of the elevator. In summary, through accurate data collection, intelligent algorithm analysis and timely fault alarm, the system significantly improves the accuracy and efficiency of elevator fault detection, providing a strong technical guarantee for the safe operation of the elevator. Description of the Drawings

[0014] Figure 1 It is a block diagram of an elevator fault detection and alarm system according to an embodiment of the present application.

[0015] Figure 2 It is a flowchart of an elevator fault detection and alarm method according to an embodiment of the present application. Specific implementation manners

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0017] As Figure 1 and Figure 2 shown, the present invention provides a technical solution: an elevator fault detection and alarm system, including an elevator data acquisition module, an elevator load and buffer state detection module, and an elevator load and buffer state analysis module; The elevator data acquisition module includes a load sensor and a linear displacement sensor, and collects elevator load data and buffer compression amount data by detecting the displacement generated by the deformation of the leaf spring and the internal measuring element of the linear displacement sensor; The elevator load and buffer state detection module analyzes the elevator load data and buffer compression amount data, learns the normal operation mode of the elevator, judges the buffer state, and establishes an elevator load and buffer state detection model based on the genetic wavelet neural network algorithm; The elevator load and buffer state analysis module judges whether the elevator is overloaded and whether the buffer is in a normal working state based on the output result of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm.

[0018] The process of the elevator data acquisition module collecting elevator load data through the load sensor includes: The load sensor is installed at the bottom of the elevator car. When the elevator bears weight, the load sensor detects the deformation of the leaf spring caused by the weight change. The deformation further generates a displacement amount. The load sensor measures this displacement amount and converts it into an output of voltage change through an internal circuit. This voltage change is proportional to the elevator load. Therefore, the elevator load is calculated by measuring the voltage change. The elevator data acquisition module is responsible for receiving the electrical signal converted by the load sensor and further processing it into a digital signal.

[0019] The process of the elevator data acquisition module monitoring the compression amount of the buffer through the linear displacement sensor includes: The linear displacement sensor monitors the buffer compression data based on the principle of precise perception and conversion of displacement by its internal measuring element. The internal measuring element of the sensor undergoes corresponding displacement changes as the buffer is compressed. This displacement change is captured by the circuit inside the sensor and converted into an electrical signal, which is proportional to the buffer compression. The elevator data acquisition module is responsible for receiving the electrical signal converted by the linear displacement sensor and further processing it into a digital signal.

[0020] The process of training the input layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm by the elevator load and buffer state detection module through analyzing elevator load data and buffer compression data includes: Clean and sort the collected elevator load data and buffer compression data, remove invalid data and outliers, normalize the data, and establish a genetic wavelet neural network model. The genetic wavelet neural network model includes an input layer, a hidden layer, and an output layer. The input layer is responsible for receiving the elevator load data and buffer compression data and converting the continuous load data and buffer compression data into vector representations.

[0021] The process of training the hidden layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm by the elevator load and buffer state detection module through performing non - linear transformation and feature extraction on the input data includes: The hidden layer of the genetic wavelet neural network model uses the Morlet wavelet function as the activation function. The wavelet coefficients obtained through wavelet analysis are used as the input of the hidden layer to capture the high - frequency and low - frequency components in the collected data, perform multi - scale analysis on the input data, and thus extract features that are discriminative for the elevator operation state and buffer state. Then, the hidden layer uses the genetic algorithm to optimize the weights and thresholds of the model. A set of combinations of neural network weights and thresholds is randomly generated as the initial population. The weights and thresholds of each individual are configured to the neural network, and the elevator load data and buffer compression data are input for training. According to the error between the fault detection result and the actual fault situation, the fitness of each individual is calculated. According to the fitness value, individuals with high fitness are selected for replication, and their excellent features are retained in the next generation. Selection and replication operations are carried out through the tournament selection method. The weights and thresholds of two individuals are partially swapped to generate new offspring, and the values of some weights and thresholds are randomly changed to increase the diversity of the population. Repeat the above genetic algorithm steps to continuously iterate and optimize the weights and thresholds of the neural network. When the iteration process ends, the individual with the highest fitness is output as the optimal solution to obtain the optimal configuration of neural network weights and thresholds. In addition, through the interaction of each neuron in the hidden layer, the extracted feature information is fused to form a higher - level feature representation and output to the output layer for final judgment.

[0022] The process of the elevator load and buffer state detection module learning the normal operation mode of the elevator, judging the buffer state, and training the output layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm includes: The output layer of the genetic wavelet neural network model receives the output data of the hidden layer and performs fault judgment according to the preset judgment rules. If the elevator load exceeds the preset elevator load threshold, it is judged that the elevator is overloaded. If the elevator load does not exceed the preset elevator load threshold, it is judged that the elevator load is normal. If the buffer compression amount exceeds the compression threshold, it is judged that the buffer is damaged. If the buffer compression amount is lower than the compression threshold, it is judged that the buffer state is normal.

[0023] The process of the elevator load and buffer state analysis module judging whether the elevator is overloaded and whether the buffer is in a normal working state based on the output result of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm includes: For large passenger elevators, the maximum load is set to 1600 kg. When the detected actual load exceeds 1600 kg, the system determines that it is overloaded, gives an overload prompt on the elevator display screen, prevents the elevator from closing and starting, and ensures the safety of passengers in case of overload. When the detected actual load is lower than 1600 kg, the system determines that the load is normal and the elevator operates normally; The compression stroke of the elevator buffer is set to be between 200 and 250 mm. When the system detects that the buffer compression amount exceeds 250 mm, the system determines that the buffer state is abnormal and sends the buffer fault information to the elevator maintenance personnel for timely repair and handling. When the system detects that the buffer compression amount is within 250 mm, the system determines that the buffer state is normal and the elevator operates normally.

[0024] It should be noted that since the load data will change with factors such as the number of passengers and the weight of goods, and the buffer compression amount data will also be affected by various factors such as elevator speed and braking impact, the wavelet analysis part in the genetic wavelet neural network algorithm is used to perform multi-scale analysis on the data. The data is decomposed into different frequency components through wavelet functions, effectively capturing the high-frequency components (the sharp change part of the load data when the elevator is suddenly overloaded) and low-frequency components (the slow fluctuation part of the normal load of the elevator) in the data, so as to extract the discriminative features for the elevator operation state and buffer state. The genetic algorithm part in the genetic wavelet neural network algorithm can globally optimize the weights and thresholds of the model. By simulating the biological evolution process, an initial population (a combination of a set of neural network weights and thresholds) is randomly generated, and through selection, crossover, and mutation operations, continuous iterative optimization is carried out. After multiple generations of evolution, a combination of weights and thresholds with higher fitness is found. During the training process of the genetic wavelet neural network algorithm, it learns the normal operation mode of the elevator, including the reasonable change range of the load and the change law of the compression amount when the buffer is working normally. Then, in actual operation, it compares the current data with the normal mode in real time. If the elevator operation state deviates from the normal mode (the compression amount of the buffer is abnormal due to long-term use and wear), it can be quickly identified and an alarm can be issued; In addition, compared with the traditional method based solely on threshold judgment, the genetic wavelet neural network algorithm can more accurately judge whether the elevator is overloaded and whether the buffer is working normally. In the buffer state detection, it can effectively distinguish between normal wear and faults of the buffer. Moreover, in the actual elevator operation environment, the multi-scale analysis and feature extraction ability of the genetic wavelet neural network algorithm for data can filter out the noise interference caused by electromagnetic interference resulting in instantaneous fluctuations in sensor data, and maintain the stable operation of the model.

[0025] An elevator fault detection and alarm method includes the following steps: S1. Detect the displacement amounts generated by the deformation of the leaf spring and the internal measuring element of the linear displacement sensor through the load sensor and the linear displacement sensor respectively, and collect the elevator load data and the buffer compression amount data; S2. Establish an elevator load and buffer state detection model based on the genetic wavelet neural network algorithm by analyzing the elevator load data and the buffer compression amount data; S3. Based on the output results of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm, judge whether the elevator is overloaded and whether the buffer is in a normal working state.

[0026] It should be noted that, in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0027] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An elevator fault detection and alarm system, characterized in that: It includes an elevator data acquisition module, an elevator load and buffer status detection module, and an elevator load and buffer status analysis module; The elevator data acquisition module includes a load sensor and a linear displacement sensor, and collects elevator load data and buffer compression data by detecting the displacement generated by the deformation of the leaf spring and the internal measuring element of the linear displacement sensor; The elevator load and buffer state detection module analyzes the elevator load data and buffer compression data, learns the normal operation mode of the elevator, determines the buffer state, and establishes an elevator load and buffer state detection model based on the genetic wavelet neural network algorithm; The elevator load and buffer state analysis module determines whether the elevator is overloaded and whether the buffer is in normal working state based on the output results of the elevator load and buffer state detection model of the genetic wavelet neural network algorithm.

2. An elevator fault detection and alarm system according to claim 1, characterized in that: The process of the elevator data acquisition module collecting elevator load data through the load sensor includes: The load sensor is installed at the bottom of the elevator car. When the elevator carries weight, the load sensor detects the deformation of the leaf spring caused by the weight change, and the deformation produces displacement. The load sensor measures the displacement and converts it into a voltage change output using an internal circuit. The voltage change is proportional to the elevator load, so the elevator load is calculated by measuring the voltage change. The elevator data acquisition module is responsible for receiving the electrical signal converted by the load sensor and further processing it into a digital signal.

3. An elevator fault detection and alarm system according to claim 2, characterized in that: The elevator data acquisition module monitors the compression amount of the buffer through the linear displacement sensor, and the process includes: The linear displacement sensor monitors the buffer compression data based on the principle of accurate perception and conversion of displacement by its internal measuring elements. The internal measuring elements undergo corresponding displacement changes as the buffer is compressed. The displacement changes are captured by the circuit inside the sensor and converted into electrical signals, which are proportional to the compression of the buffer. The elevator data acquisition module is responsible for receiving the electrical signals converted by the linear displacement sensor and further processing them into digital signals.

4. An elevator fault detection and alarm system according to claim 3, characterized in that: The elevator load and buffer state detection module analyzes the elevator load data and the buffer compression data to train the input layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm, including: The collected elevator load data and buffer compression data are cleaned and sorted, invalid data and outliers are removed, the data are normalized, and a genetic wavelet neural network model is established. The genetic wavelet neural network model includes an input layer, a hidden layer and an output layer, wherein the input layer is responsible for receiving the elevator load data and buffer compression data, and converting the continuous load data and buffer compression data into vector representation.

5. An elevator fault detection and alarm system according to claim 4, characterized in that: The elevator load and buffer state detection module performs nonlinear transformation and feature extraction on input data, and the process of training the hidden layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm includes: The hidden layer of the genetic wavelet neural network model uses the Morlet wavelet function as the activation function, and uses the wavelet coefficients obtained through wavelet analysis as the input of the hidden layer to capture the high-frequency and low-frequency components in the collected data, and perform multi-scale analysis on the input data to extract the distinguishing features of the elevator operation status and buffer status. Then, the hidden layer uses the genetic algorithm to optimize the weights and thresholds of the model, randomly generates a set of neural network weights and threshold combinations as the initial population, assigns the weights and thresholds of each individual to the neural network, and inputs the elevator load data and buffer compression data for training. According to the error between the fault detection result and the actual fault condition, the fitness of each individual is calculated. According to the fitness size, individuals with high fitness are selected for replication, and their excellent characteristics are retained in the next generation. The selection and replication operations are performed through the tournament selection method. The weights and thresholds of the two individuals are partially exchanged to generate new offspring. The values ​​of some weights and thresholds are randomly changed to increase the diversity of the population. The above genetic algorithm steps are repeated to continuously iterate and optimize the weights and thresholds of the neural network. When the iterative process ends, the individual with the highest fitness is output as the optimal solution, and the optimal neural network weight and threshold configuration is obtained. In addition, through the interaction of each neuron in the hidden layer, the extracted feature information is fused to form a higher-level feature representation, which is output to the output layer for final judgment.

6. An elevator fault detection and alarm system according to claim 5, characterized in that: The process of the elevator load and buffer state detection module learning the normal operation mode of the elevator, judging the buffer state, and training the output layer of the elevator load and buffer state detection model based on the genetic wavelet neural network algorithm includes: The output layer of the genetic wavelet neural network model receives the output data of the hidden layer and makes a fault judgment according to the preset judgment rules. If the elevator load exceeds the preset elevator load threshold, the elevator is judged to be overloaded. If the elevator load does not exceed the preset elevator load threshold, the elevator load is judged to be normal. If the buffer compression exceeds the compression threshold, the buffer is judged to be damaged. If the buffer compression is lower than the compression threshold, the buffer is judged to be in normal state.

7. An elevator fault detection and alarm system according to claim 6, characterized in that: The elevator load and buffer state analysis module determines whether the elevator is overloaded and whether the buffer is in normal working state based on the output results of the elevator load and buffer state detection model of the genetic wavelet neural network algorithm. The process includes: For large passenger elevators, the maximum load is set to 1600kg. When the actual load is detected to be more than 1600kg, the system determines it as overloaded, and an overload prompt is displayed on the elevator display screen to prevent the elevator from closing the door and starting, ensuring the safety of passengers in overloaded conditions. When the actual load is detected to be less than 1600kg, the system determines it as normal load and the elevator runs normally. The elevator buffer compression stroke is set between 200 and 250 mm. When the system detects that the buffer compression exceeds 250 mm, the system determines that the buffer is in an abnormal state and sends the buffer fault information to the elevator maintenance personnel for timely repair and processing. When the system detects that the buffer compression is within 250 mm, the system determines that the buffer is in a normal state and the elevator operates normally.

8. An elevator fault detection and alarm method, characterized in that: The following steps are involved: S1. Detect the displacement caused by the deformation of the leaf spring and the internal measuring element of the linear displacement sensor respectively through the load sensor and the linear displacement sensor, and collect the elevator load data and the buffer compression data; S2. By analyzing the elevator load data and buffer compression data, an elevator load and buffer state detection model based on the genetic wavelet neural network algorithm is established; S3. The output results of the elevator load and buffer status detection model based on the genetic wavelet neural network algorithm are used to determine whether the elevator is overloaded and whether the buffer is in normal working condition.

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