Complex system fault positioning method based on Isin model

Abstract

The invention discloses a complex system fault positioning method based on an Isin model, and the method comprises the steps: enabling each sensor in a complex system to correspond to a node in the Isin model, and representing the state of the sensor through a spin variable; determining interaction parameters in the Isin model according to the correlation between different sensors and the variation trend of the sensor readings; defining an energy function, the energy function including an interaction term, an external field term, and a term related to sensor readings; defining a loss function; parameters are initialized randomly; minimizing a loss function by using an optimization algorithm, and updating parameters; iteratively updating the node state by adopting a Monte Carlo method, and updating the spinning state of the node at a preset probability according to the current energy function and the temperature parameter; and determining the fault state of the sensor according to the final node spinning state. According to the invention, rapid fault positioning of a complex system can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of fault diagnosis, and in particular relates to a complex system fault locating method based on an Ising model. Background Art

[0002] With the continuous development of modern industry and technology, complex systems are increasingly used in various fields. For example, in complex systems such as air-conditioning units, multiple sensors are usually equipped to monitor different physical parameters, such as flow, temperature, humidity, etc. The data from these sensors is crucial for the normal operation and fault diagnosis of the system. However, traditional fault diagnosis methods often have some limitations when facing complex systems. On the one hand, for systems with a large number of sensors and complex structures, traditional methods may find it difficult to accurately locate the source of the fault, and require a lot of time and manpower for troubleshooting. On the other hand, traditional methods may not be able to fully utilize the correlation between sensors and the physical characteristics of the system, resulting in low accuracy and efficiency of fault diagnosis. Therefore, there is an urgent need for a new fault location method that can make full use of the data of sensors in complex systems, combined with advanced mathematical models and algorithms, to achieve rapid and accurate fault location. As a physical model, the Ising model has certain potential in dealing with problems of complex systems.

[0003] The invention with publication number CN118487315A discloses a quantum-classical hybrid solution method, system, device and storage medium for power system unit combination to resist extreme events, the invention with publication number US10539468B2 discloses an anomaly detection device, an anomaly detection method, and a non-temporary computer-readable medium, and the invention with publication number CN113691311B discloses a fault location method, electronic device and computer-readable storage medium for optical networks, all of which propose the possibility of applying the Ising model to the field of faults. However, these studies either need to build extremely complex system operation models, resulting in a sharp increase in the amount of calculation and poor real-time performance, or can only be applied to some simple system structures and cannot solve the problem of fault location in complex systems. Summary of the invention

[0004] Technical problem to be solved: In order to solve the technical problem of fault location in complex systems, the present invention proposes a complex system fault location method based on the Ising model. The sensors in the complex system are mapped to nodes in the Ising model, and the interaction parameters are defined according to the correlation and physical properties between the sensors. The characteristics of the Ising model are used to analyze the state of the system, thereby achieving rapid fault location.

[0005] Technical solution:

[0006] A complex system fault location method based on the Ising model, the complex system fault location method comprising the following steps:

[0007] S1, collects the readings of each sensor in the complex system, and records the corresponding timestamps and equipment operation status tags; cleans the collected sensor data to remove outliers and noise; normalizes the data of different sensors to make them have the same scale range;

[0008] S2, each sensor in the complex system corresponds to a node in the Ising model, and the state of the sensor is represented by a spin variable, where spin up represents a normal state and spin down represents a fault state;

[0009] S3, determining the interaction parameters in the Ising model according to the correlation between different sensors and the changing trend of sensor readings;

[0010] S4, defining an energy function, the energy function including interaction terms, external field terms, and terms related to sensor readings;

[0011] S5, defining a loss function according to the actual device operation state label, the state predicted by the Ising model, and the energy function;

[0012] S6, randomly initialize spin variables, interaction parameters, external field parameters, and parameters related to sensor readings;

[0013] S7, using an optimization algorithm to minimize the loss function and update the interaction parameters, external field parameters, and parameters related to the sensor readings;

[0014] S8, using the Monte Carlo method to iteratively update the node state, and updating the node spin state with a preset probability according to the current energy function and temperature parameters;

[0015] S9, repeat the iteration until the system reaches thermal equilibrium or meets certain convergence conditions;

[0016] S10, determine the fault state of the sensor according to the final node spin state

[0017] Furthermore, in step S1, the sensor data includes but is not limited to flow, temperature, and humidity data.

[0018] Furthermore, in step S1, the process of cleaning the collected sensor data includes:

[0019] Set a threshold to filter out abnormal values ​​in the sensor data, and then use statistical methods to remove noise in the sensor data.

[0020] Furthermore, in step S1, the data of different sensors are normalized using the following formula:

[0021]

[0022] Where x is the original data, x min and x max are the minimum and maximum values ​​of the data respectively.

[0023] Further, in step S4, the energy function is:

[0024]

[0025] Among them J ij is the interaction parameter, s i is the node spin variable, h i is the external field parameter, d i is the difference between the sensor reading and the expected value, and λ is the adjustment parameter.

[0026] Further, in step S5, the loss function is:

[0027] L total =αL CE +(1-α)E

[0028]

[0029] The weight parameter α balances the cross entropy loss L CE and the contribution E of the energy function; C is the total number of categories, y ic is an indicator variable, is the predicted probability that sample i belongs to category c.

[0030] Furthermore, in step S7, the gradient descent method is used to calculate the gradients of the interaction parameters, the external field parameters, and the parameters related to the sensor readings and update them respectively. The updating process includes the following steps:

[0031] S71, calculate the loss function for the interaction parameter J ij The gradient is:

[0032]

[0033] S72, calculate the loss function and the external field parameter h i The gradient is:

[0034]

[0035] S73, calculate the gradient of the loss function with respect to the parameters related to the sensor readings:

[0036]

[0037] S74, updating the interaction parameters, external field parameters, and parameters related to the sensor readings;

[0038] For the interaction parameter J ij , the update formula is: Where α is the learning rate;

[0039] For the external field parameter h i , the update formula is:

[0040] For the parameter λ related to the sensor reading, the update formula is:

[0041] Further, in step S8, the process of updating the spin state of the node includes the following steps:

[0042] S81, calculate sensor reading x i With the expected value μ i The difference d i :d i =x i -μ i ;

[0043] S82, calculating the terms in the energy function related to the sensor readings

[0044] S83, randomly initialize the node spin state to up or down, using Indicates the initial state;

[0045] S84, using the Monte Carlo method to iteratively update the node state; in each iteration, randomly select a node, and update the spin state of the node with a certain probability according to the current energy function and temperature parameter T;

[0046] The probability of updating the node spin state is calculated using the Metropolis algorithm:

[0047] P(s i →-s i )=min(1,e -ΔE / T )

[0048] Where ΔE is the energy change and T is the temperature parameter.

[0049] Furthermore, in step S10, determining the fault state of the sensor according to the final node spin state means that if the spin variable s corresponding to a certain sensor is i = -1, the sensor is considered to be faulty.

[0050] Beneficial effects:

[0051] First, the complex system fault location method based on the Ising model of the present invention combines the sensor network with the Ising model and utilizes the characteristics of sensor readings and the Ising model to accurately and efficiently locate possible faults in the complex system. It is particularly suitable for complex systems with multiple sensors, such as air-conditioning units.

[0052] Second, the complex system fault location method based on the Ising model of the present invention covers multiple technical links such as data collection and processing, model construction, parameter training and fault location. It can be widely used in industrial automation, intelligent equipment maintenance, Internet of Things and other fields, and provides strong technical support for ensuring the stable operation and timely maintenance of complex systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a framework diagram of the complex system fault location method based on the Ising model of the present invention.

[0054] Figure 2 A diagram showing the topology of a sensor network described using the two-dimensional Ising model. DETAILED DESCRIPTION

[0055] The following examples may enable those skilled in the art to more fully understand the present invention, but are not intended to limit the present invention in any way. Figure 1 The present invention discloses a complex system fault location method based on the Ising model, comprising the following steps:

[0056] (1) Collect the readings of various sensors in the complex system, including but not limited to flow, temperature, humidity and other data, and record the corresponding timestamps and equipment operation status tags.

[0057] (2) Cleaning the collected sensor data to remove outliers and noise; preferably, the method for cleaning the collected sensor data includes setting a threshold to filter outliers and using a statistical method to remove noise.

[0058] (3) Normalize the data from different sensors to make them have the same scale range. The data normalization can be done using the min-max normalization method, with the formula:

[0059]

[0060] Where x is the original data, x min and x max are the minimum and maximum values ​​of the data respectively.

[0061] (4) Each sensor in the complex system corresponds to a node in the Ising model, and the state of the sensor is represented by a spin variable, where spin-up represents a normal state and spin-down represents a fault state. Figure 2 shown.

[0062] (5) Determine the interaction parameters in the Ising model based on the correlation between sensors and sensor readings; specifically, the interaction parameters are determined based on the correlation coefficient between different sensor readings and considering the change trend of the sensor readings.

[0063] (6) Define an energy function, which includes interaction terms, external field terms, and terms related to sensor readings. The energy function is:

[0064]

[0065] Among them J ij is the interaction parameter, s i is the node spin variable, h i is the external field parameter, d i is the difference between the sensor reading and the expected value, and λ is the adjustment parameter.

[0066] (7) Define the loss function based on the actual device state label, the state predicted by the model, and the energy function.

[0067] The loss function of the present invention should consider both the energy function and the cross-entropy loss function. The energy function plays an important role in the Ising model. It determines the state of the system and the spin state of the node. During the training process, the value of the energy function is also affected by adjusting the model parameters. Normally, when the system reaches a stable state, the energy function will be at a lower value. In fault location, it is hoped that the system will be in a more reasonable state by minimizing the energy function, so as to determine the faulty node. On the other hand, minimizing the cross-entropy loss function is to make the model's prediction results as close as possible to the actual device status label, thereby improving the accuracy of fault location. The cross-entropy loss function measures the difference between the probability distribution predicted by the model and the actual label. The model parameters are continuously adjusted through the optimization algorithm so that the loss function value gradually decreases. For multi-classification problems, the formula of the cross-entropy loss function is:

[0068]

[0069] Where C is the total number of categories, y ic is an indicator variable (if the true category of sample i is equal to c, then yic = 1, otherwise 0), is the predicted probability that sample i belongs to category c. Therefore, the total loss function is:

[0070] Ltotal =αL CE +(1-α)E

[0071] The weight parameter α balances the contribution of the cross entropy loss and the energy function.

[0072] (8) Randomly initialize spin variables, interaction parameters, external field parameters, and parameters related to sensor readings.

[0073] (9) Use optimization algorithm to minimize the loss function and update the parameters.

[0074] The optimization algorithm can use the gradient descent method to calculate and update the gradients of the interaction parameters, external field parameters, and parameters related to sensor readings. The process includes:

[0075] (9.1) First, calculate the loss function for the interaction parameter J ij The gradient is:

[0076] Assuming that the relationship between the loss function and the energy function is known, it can be calculated using the chain rule. The calculation of usually involves taking the partial derivative of the energy function with respect to and then deriving the dependency of the loss function on the energy function.

[0077] Energy function E with respect to J ij Taking partial derivatives we get:

[0078] For the cross entropy loss function L CE About J ij The gradient of J usually needs to be combined with the model prediction probability through the chain rule ij The dependency relationship is calculated, let it be Then the total loss function is the interaction parameter J ij The gradient of is:

[0079]

[0080] (9.2) Calculate the loss function and the external field parameter h i The gradient is:

[0081] The energy function E with respect to h i Taking partial derivatives we get:

[0082] For the cross entropy loss function L CE About h i The gradient of

[0083] The total loss function is the external field parameter h i The gradient of is:

[0084]

[0085] (9.3) Calculate the gradient of the loss function with respect to the parameters related to the sensor readings (such as the adjustment parameter λ, etc.):

[0086] Taking the partial derivative of the energy function E with respect to λ, we can get:

[0087] For the cross entropy loss function L CE The gradient with respect to λ is set to

[0088] Then the gradient of the total loss function with respect to λ is:

[0089]

[0090] (9.4) Parameter update formula:

[0091] For the interaction parameter J ij : Where α is the learning rate.

[0092] For the external field parameter h i :

[0093] For parameters related to sensor readings (such as the tuning parameter λ):

[0094] (10) The Monte Carlo method is used to iteratively update the node state. According to the current energy function and temperature parameters, the spin state of the node is updated with a certain probability. The specific process of updating the spin state of the node is:

[0095] (10.1) Calculate the reading x of sensor i i and the expected value μ i The difference d i :d i =x i -μ i

[0096] (10.2) Calculate the terms in the energy function related to the sensor readings:

[0097] The known energy function is Among them J ij is the interaction parameter, s i is the node spin variable, h i is the external field parameter and λ is the adjustment parameter. The term related to the sensor reading is

[0098] (10.3) Initialize the node spin state: The node spin state can be randomly initialized to up (normal state) or down (fault state), using Indicates the initial state.

[0099] (10.4) Iteratively update the spin state:

[0100] In each iteration, a node is randomly selected and its spin state is updated with a certain probability according to the current energy function and temperature parameter T. The probability of updating the node spin state is calculated using the Monte Carlo method (Metropolis algorithm), and the formula is:

[0101] P(s i →-s i )=min(1,e -ΔE / T )

[0102] Where ΔE is the energy change and T is the temperature parameter.

[0103] After many iterations, the system reaches a relatively stable state.

[0104] (11) Repeat the iteration until the system reaches thermal equilibrium or meets certain convergence conditions.

[0105] (12) Determine the fault state of the sensor based on the final node spin state.

[0106] The fault state of the sensor is determined based on the final node spin state. Specifically, if the spin variable s corresponding to a sensor is i = -1, the sensor is considered to be faulty.

[0107] The above are only preferred embodiments 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 be regarded as the protection scope of the present invention.

Claims

1. A complex system fault location method based on the Ising model, characterized in that: The complex system fault location method comprises the following steps: S1, collects the readings of each sensor in the complex system, and records the corresponding timestamps and equipment operation status tags; cleans the collected sensor data to remove outliers and noise; normalizes the data of different sensors to make them have the same scale range; S2, each sensor in the complex system corresponds to a node in the Ising model, and the state of the sensor is represented by a spin variable, where spin up represents a normal state and spin down represents a fault state; S3, determining the interaction parameters in the Ising model according to the correlation between different sensors and the changing trend of sensor readings; S4, defining an energy function, the energy function including interaction terms, external field terms, and terms related to sensor readings; S5, defining a loss function according to the actual device operation state label, the state predicted by the Ising model, and the energy function; S6, randomly initialize spin variables, interaction parameters, external field parameters, and parameters related to sensor readings; S7, using an optimization algorithm to minimize the loss function and update the interaction parameters, external field parameters, and parameters related to the sensor readings; S8, using the Monte Carlo method to iteratively update the node state, and updating the node spin state with a preset probability according to the current energy function and temperature parameters; S9, repeat the iteration until the system reaches thermal equilibrium or meets certain convergence conditions; S10, determining the fault state of the sensor according to the final node spin state.

2. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S1, the sensor data includes but is not limited to flow rate, temperature, and humidity data.

3. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S1, the process of cleaning the collected sensor data includes: Set a threshold to filter out abnormal values ​​in the sensor data, and then use statistical methods to remove noise in the sensor data.

4. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S1, the data from different sensors are normalized using the following formula: Where x is the original data, x min and x max are the minimum and maximum values ​​of the data respectively.

5. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S4, the energy function is: Among them J ij is the interaction parameter, s i is the node spin variable, h i is the external field parameter, d i is the difference between the sensor reading and the expected value, and λ is the adjustment parameter.

6. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S5, the loss function is: L total =αL CE +(1-a)E The weight parameter α balances the cross entropy loss L CE and the contribution E of the energy function; C is the total number of categories, y ic is an indicator variable, is the predicted probability that sample i belongs to category c.

7. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S7, the gradient descent method is used to calculate the gradients of the interaction parameters, the external field parameters, and the parameters related to the sensor readings and update them. The updating process includes the following steps: S71, calculate the loss function for the interaction parameter J ij The gradient is: S72, calculate the loss function and the external field parameter h i The gradient is: S73, calculate the gradient of the loss function with respect to the parameters related to the sensor readings: S74, updating the interaction parameters, external field parameters, and parameters related to the sensor readings; For the interaction parameter J ij , the update formula is: Where α is the learning rate; For the external field parameter h i , the update formula is: For the parameter λ related to the sensor reading, the update formula is:

8. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S8, the process of updating the spin state of the node includes the following steps: S81, calculate sensor reading x i and the expected value μ i The difference d i :d i =x i -μ i ; S82, calculating the terms in the energy function related to the sensor readings S83, randomly initialize the node spin state to up or down, using Indicates the initial state; S84, using the Monte Carlo method to iteratively update the node state; in each iteration, randomly select a node, and update the spin state of the node with a certain probability according to the current energy function and temperature parameter T; The probability of updating the node spin state is calculated using the Metropolis algorithm: P(s i →-s i )=min(1,e -ΔE / T ) Where ΔE is the energy change and T is the temperature parameter.

9. The complex system fault location method based on the Ising model according to claim 1, characterized in that: In step S10, determining the fault state of the sensor according to the final node spin state means that if the spin variable s corresponding to a sensor is i = -1, the sensor is considered to be faulty.

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