Multi-sensor integrated system for remote monitoring of industrial equipment

Abstract

The invention relates to the field of industrial equipment monitoring, and discloses a multi-sensor integrated system for remote monitoring of industrial equipment, which comprises a data acquisition module, a data processing module, a fault prediction module, a self-adaptive control module and a monitoring feedback module, the data acquisition module acquires multi-dimensional data from sensor arrays of a plurality of devices; the data processing module carries out noise reduction processing on the data and extracts key features; the fault prediction module predicts a short-term fault of the equipment and predicts a long-term operation trend; the self-adaptive control module adjusts control parameters of the equipment according to the prediction result and optimizes a control strategy; and the monitoring feedback module generates real-time fault alarm information by realizing remote monitoring and feedback of the equipment. The system can improve the safety and stability of equipment operation, optimize equipment management and reduce the equipment maintenance cost, and has high real-time performance, accuracy and intelligent level.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment monitoring, and in particular to a multi-sensor integrated system for remote monitoring of industrial equipment. Background Art

[0002] In modern industrial production, the operating status of industrial equipment directly affects production efficiency and product quality, so real-time monitoring and maintenance of equipment is particularly important. Traditional equipment monitoring methods usually rely on a single type of sensor or regular manual inspections, which have many limitations when dealing with equipment failure prediction and equipment status monitoring. For example, the use of a single sensor may not be able to fully reflect the complex status of the equipment and effectively capture the multi-dimensional changes that may occur during equipment operation. In addition, equipment status data often contains noise and redundant information, resulting in inefficient data processing and affecting the accuracy and real-time nature of fault prediction.

[0003] Existing monitoring systems often face problems such as data redundancy and inability to respond to changes in equipment status in a timely manner when processing massive amounts of data. For large industrial scenarios, especially environments where multiple devices work together, traditional centralized monitoring architectures lack flexibility and scalability when it comes to real-time data processing and global optimization. In addition, existing control strategies are usually based on preset fixed parameters and cannot be dynamically adjusted according to changes in equipment status, making it difficult for equipment to adapt to changes in working conditions during operation, ultimately affecting the system's operating efficiency and equipment safety. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a multi-sensor integrated system for remote monitoring of industrial equipment. Through multi-sensor data acquisition and fusion, intelligent fault prediction, adaptive control and real-time feedback mechanism, it can realize comprehensive monitoring and dynamic optimization of industrial equipment, ensuring the safety and efficient operation of equipment under complex working conditions.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-sensor integrated system for remote monitoring of industrial equipment, comprising:

[0006] Data acquisition module, used to collect multiple sensor data of industrial equipment, including temperature, vibration, pressure and energy consumption parameters;

[0007] A data processing module, used to perform noise reduction, data fusion and dimension reduction processing on the collected sensor data to generate real-time status information of the device;

[0008] A fault prediction module, used to predict short-term and long-term faults of equipment based on the real-time status information and provide fault warning information;

[0009] An adaptive control module, used to dynamically adjust the operating parameters of the equipment according to the real-time status information and the fault prediction result, so as to realize adaptive control of the equipment;

[0010] The monitoring feedback module is used to monitor the multiple devices in real time and generate alarm information to feedback the device status to the operator.

[0011] The present invention also provides a multi-sensor integration method for remote monitoring of industrial equipment, comprising the following steps:

[0012] Collect multi-dimensional data including temperature, vibration, pressure and energy consumption from sensor arrays of multiple devices;

[0013] Using Kalman filtering to perform noise reduction on the data and predict the current state of the device;

[0014] Using principal component analysis to reduce the dimensionality of the multi-dimensional data to generate low-dimensional feature data;

[0015] Predict short-term equipment failures based on hidden Markov models, and predict long-term equipment operating trends based on long short-term memory networks;

[0016] According to the prediction results, the control parameters of the equipment are adjusted through Lyapunov adaptive control, and the control strategy is optimized through distributed reinforcement learning;

[0017] Through edge computing and cloud collaboration, remote monitoring and feedback of equipment can be achieved, and fault alarm information can be generated.

[0018] The present invention provides a multi-sensor integrated system for remote monitoring of industrial equipment. It has the following beneficial effects:

[0019] 1. The present invention can collect multi-dimensional operation data of the equipment in real time by integrating multiple sensors, including key parameters such as temperature, vibration, pressure and energy consumption. Through high-frequency sensor data collection, the system can fully cover the operation status of the equipment, ensuring that the operation status of the equipment can be captured and monitored in time, providing a reliable data basis for subsequent state estimation and control adjustment.

[0020] 2. The present invention uses Kalman filtering to reduce the noise of equipment data, effectively eliminating random noise in sensor data and ensuring data accuracy. At the same time, principal component analysis technology is used to reduce data dimensionality, extract key features in multi-dimensional data, and reduce redundant information. This not only improves the efficiency of data processing, but also provides concise input data for fault prediction and control, and improves the response speed and processing capacity of the system.

[0021] 3. The present invention predicts short-term equipment failures through hidden Markov models and analyzes the long-term trends of equipment through long-term and short-term memory networks. The system can comprehensively predict the health status of equipment and potential failure risks. The failure prediction mechanism can provide early warnings to help operators take necessary maintenance measures before failures occur, reduce unplanned downtime of equipment, and reduce maintenance costs.

[0022] 4. The present invention uses the Lyapunov adaptive control algorithm. The system can dynamically adjust the control parameters according to the real-time status of the equipment to ensure the stable operation of the equipment under different working conditions. In addition, through distributed reinforcement learning, the system continuously learns and optimizes the control strategy of the equipment, so that the equipment can adaptively respond to changing environmental conditions and achieve higher operating efficiency and safety.

[0023] 5. The present invention can remotely monitor and feedback the equipment through edge computing and cloud collaboration architecture. While achieving rapid response locally, the cloud performs global optimization and collaborative control. When equipment abnormalities or potential failures are detected, the system can generate real-time alarm information and feed it back to the operator to ensure that the failure is handled in a timely manner to avoid equipment damage or production interruption. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0025] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Please see attached Figure 1 The embodiment of the present invention provides a multi-sensor integrated system for remote monitoring of industrial equipment, which centrally monitors and optimizes the control of multiple industrial equipment through the synergy of modules such as multi-sensor data acquisition, data processing, fault prediction and adaptive control. The system integrates multiple sensors to obtain the operating status of industrial equipment in real time, and uses signal processing, fault prediction and control strategies to ensure the efficient operation and safety of the equipment. The following is a detailed description of each module of the system of the present invention.

[0028] Data acquisition module

[0029] First, it is necessary to collect a variety of sensor data from industrial equipment to provide basic support for centralized monitoring of multiple devices and comprehensive factory control. This module plays the role of data input in the entire system, ensuring that subsequent data processing, fault prediction and adaptive control modules can obtain accurate real-time equipment status information to achieve global optimization and stable control.

[0030] In this embodiment, the data acquisition module integrates multiple sensors from industrial equipment to collect data, including but not limited to key parameters such as temperature, vibration, pressure and energy consumption.

[0031] The data acquisition module uses a multi-channel data acquisition device to connect multiple types of sensors, which measure different physical quantities of industrial equipment, including temperature, vibration, pressure and energy consumption. Each sensor corresponds to an independent data acquisition channel to ensure that each type of data can be independently collected and stored, while meeting the real-time requirements between different physical quantity data.

[0032] In this embodiment, the data acquisition module is connected to the data processing module through the edge computing node, and the collected data is transmitted to the data processing module in real time to achieve rapid data preprocessing and analysis.

[0033] The data acquisition module in the present invention undertakes the basic task of obtaining the operating status of the equipment. Through multi-sensor integration, reasonable sampling frequency setting, preliminary preprocessing, time synchronization and anomaly detection, it ensures high-quality data input for subsequent data processing, fault prediction and adaptive control.

[0034] Data processing module

[0035] The data processing module processes and optimizes the raw sensor data provided by the data acquisition module, including noise reduction, data fusion and dimensionality reduction.

[0036] In this embodiment, the data processing module is used to process the sensor data of multiple devices transmitted by the acquisition module, and includes two core units: Kalman filtering and principal component analysis (PCA).

[0037] Kalman filter unit

[0038] In this embodiment, the data processing module includes a Kalman filter unit, which is used to perform real-time noise filtering and state estimation on sensor data of multiple devices to ensure that the generated device state information accurately reflects the operation status of the device. The processing process of the Kalman filter mainly includes the following four steps:

[0039] State prediction: Based on the state transition equation of the device, the state of the device is predicted. By establishing the state transition model of the device, the current state of the device x kThrough the state transfer matrix A and control input u k Make predictions:

[0040] x k+1 =Ax k +Bu k +w k

[0041] Among them, x k is the state vector at time k, B is the control input matrix, u k is the control input, w k is process noise, which is usually assumed to be Gaussian white noise with zero mean and covariance Q.

[0042] Observation prediction: According to the observation equation of the device sensor, predict the observation value of the sensor at time k. This step is used to estimate the theoretical observation value of the sensor under a given device state. The formula is:

[0043] z k =Hx k +v k

[0044] Among them, z k is the sensor’s observation value, H is the observation matrix, v k For the observation noise, it is usually assumed to be Gaussian white noise with zero mean and covariance R.

[0045] Status update: based on the actual observed value z k , combined with the Kalman gain K k , update the state of the device to correct the error between the predicted value and the actual observed value. The state update formula is:

[0046]

[0047] in, is the predicted state, K k is the Kalman gain, and its calculation formula is:

[0048] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0049] This step combines the prediction and observation errors to obtain the current optimal state estimate.

[0050] Covariance update: Based on the covariance matrix after the state update, the covariance of the prediction error is updated to reflect the accuracy of the latest state estimate. The covariance update formula is:

[0051] P k|k =(IK k H)P k|k-1

[0052] Among them, P k|k-1 is the covariance matrix of state prediction, P k|k is the updated covariance matrix, and I is the identity matrix.

[0053] Through the above steps, the Kalman filter unit can dynamically estimate the equipment status in real time and effectively filter noise to ensure that the status information input to the fault prediction module is accurate and reliable.

[0054] Principal Component Analysis (PCA) Unit

[0055] In this embodiment, the data processing module also includes a principal component analysis (PCA) unit, which is used to perform dimensionality reduction processing on high-dimensional data of multiple device sensors, reduce data redundancy and extract key features.

[0056] The processing of the PCA unit includes the following steps:

[0057] Data centering: The raw data collected by multiple device sensors are centrally processed, the mean of each sensor is calculated, and the data matrix X is decentralized to obtain in Represents the decentralized data matrix, n is the number of data samples, and p is the number of sensors. After removing the mean of the data, we get a decentralized data matrix

[0058]

[0059] Among them, μ X is the mean vector of the data.

[0060] Calculation of the covariance matrix: Based on the decentralized data Compute the covariance matrix C of the sensor data to capture the correlation between dimensions in the data:

[0061]

[0062] in, is the transpose of the data matrix, is the covariance matrix of the p sensor data.

[0063] Eigendecomposition: Perform eigenvalue decomposition on the covariance matrix C and solve the eigenvalue λ i and the corresponding eigenvector v i , where λ iis the i-th eigenvalue of the covariance matrix C, v i For the corresponding eigenvector, arrange the eigenvectors to form a feature matrix V;

[0064] Dimensionality reduction: Select the eigenvectors corresponding to the first k largest eigenvalues ​​to form a dimensionality reduction matrix And high-dimensional data The dimension reduction is performed through the matrix V, and the formula is:

[0065]

[0066] in, is the data matrix after dimensionality reduction, and k is the number of feature dimensions retained after dimensionality reduction. Through this process, PCA can effectively reduce the data dimension, reduce redundant information, and retain the main change characteristics in the data.

[0067] Through the principal component analysis unit, the system can significantly reduce the dimension of sensor data while ensuring the key features of the data, so that subsequent fault prediction and adaptive control modules can process this data with less computing burden, while improving the overall response speed of the system.

[0068] Fault prediction module

[0069] The fault prediction module evaluates the health of the equipment and identifies potential faults in advance based on the real-time status information provided by the data processing module through short-term and long-term prediction algorithms. The core function of this module is to provide fault warning information by analyzing the trend of equipment status changes so that necessary maintenance measures can be taken before the equipment fails.

[0070] In this embodiment, the fault prediction module uses a hidden Markov model (HMM) to predict faults on the short-term state of the equipment, and uses a long short-term memory network (LSTM) to perform trend analysis on the long-term state of the equipment.

[0071] Hidden Markov Model (HMM) Unit

[0072] In this embodiment, the Hidden Markov Model (HMM) unit in the fault prediction module is used to predict the short-term faults of the equipment. HMM is suitable for processing time series data with hidden states, and is used in this system to predict the short-term changes in the operating state of the equipment. HMM predicts the possible faults of the equipment in the future by modeling the state transition process of the equipment. The specific steps are as follows:

[0073] State definition: Set the possible hidden state set of the device to S = S1, S2, ..., S N , where each S iIndicates different operating states of the device, such as normal state, slightly abnormal state, serious fault state, etc. N is the number of hidden states, state S i It cannot be observed directly, but can be inferred indirectly through sensor observation data.

[0074] State transition probability matrix: Define the state transition probability matrix A = [a ij ], where a ij Indicates that the device status changes from S i Transfer to S j The probability of satisfying:

[0075] a ij =P(S j |S i )

[0076] And the state transfer matrix A satisfies the following conditions:

[0077]

[0078] Among them, i, j = 1, 2, ..., N, indicating that the sum of the transition probabilities between all states is 1. This matrix describes the transition law of the device between different hidden states.

[0079] Observation probability matrix: the data sequence O1, O2, ..., O2 output by the device sensor T As the observation value of HMM, define the observation probability matrix B = [b j (o)], indicating that in the hidden state S j The probability of observing sensor data o is:

[0080] b j (o) = P(O t =o|S t =S j )

[0081] Among them, b j (o) is in the hidden state S j The probability of observing data o under , where o is the observed value of the device.

[0082] Initial state probability: Define the initial state probability vector π = [π i ],π i Indicates that the device is initially in the hidden state S i The probability of satisfying:

[0083]

[0084] Among them, π i =P(S1=S i ) indicates that at time 1 the device is in state Si The probability of this vector describes the initial operating state distribution of the equipment.

[0085] The forward algorithm predicts the device state: The forward algorithm is used to calculate the hidden state S of the device at time t. i The probability α t (S i ), the formula is:

[0086]

[0087] Among them, α t-1 (S t-1 ) is the state S of the device at time t-1 t-1 The probability of t-1，t From state S t-1 Transfer to S t The probability of i (O t ) is in state S i Observed O t probability.

[0088] In some embodiments, the observation probability calculation mechanism of the hidden Markov model unit plays a vital role in the fault prediction of the device. This mechanism calculates the observation probability of the current state of the device in real time based on multiple sensor data of the device through multi-sensor fusion processing, thereby improving the accuracy of device state estimation.

[0089] In this embodiment, the observation probability calculation mechanism of the hidden Markov model unit includes the fusion processing of multi-sensor data. Through the real-time collection and fusion of multi-sensor data, the system can comprehensively analyze the different physical quantities of the equipment and use them to estimate the operating status of the equipment in real time. The observation probability calculation is mainly based on the comprehensive observation value obtained after the fusion of sensor data, combined with the observation probability model of the hidden state, and the following steps are calculated:

[0090] 1. Multi-sensor data fusion

[0091] In the actual operation of industrial equipment, the data from a single sensor may not fully reflect the actual status of the equipment. Therefore, this embodiment adopts multi-sensor fusion technology to combine and process the data from different sensors of the equipment. Set the observation value sequence of the equipment to O1, O2, ..., O T ,These sensor data include different physical quantities such as temperature, vibration, pressure and energy consumption.

[0092] Through multi-sensor data fusion, the system can generate a comprehensive observation value Its expression is:

[0093]

[0094] in, is the observation value of the i-th sensor at time t, f(·) is the multi-sensor data fusion function, and n is the number of sensors on the device. This function can be a weighted average, filter fusion, or a processing method based on statistical features. The specific implementation method is adjusted according to the characteristics of the device and sensor.

[0095] 2. Calculation of observation probability

[0096] According to the integrated observation value after fusion Calculate the current hidden state S of the device j The probability of observation under In the hidden Markov model, the observation probability reflects the degree of conformity between the sensor observation value and the state when the device is in a certain hidden state. The calculation formula of the observation probability is:

[0097]

[0098] Among them, S j is the hidden state of the device at time t, The device is in state S j The comprehensive observation value is observed probability.

[0099] Specifically, The calculation of is based on the observation data of each sensor, and the conditional independence assumption can be used to decompose the multi-sensor observation probability:

[0100]

[0101] in, The device is in hidden state S j Next, the data is observed by the i-th sensor probability.

[0102] 3. Real-time update of observation probability

[0103] In this embodiment, HMM updates the observation probability of the device in real time through the forward algorithm. By combining the real-time data of the sensor and the state transition probability, the system can predict the hidden state of the device at the future moment and dynamically adjust the observation probability. The update formula of the forward algorithm is:

[0104]

[0105] Among them, α t-1 (S t-1 ) is the state S of the device at time t-1 t-1 The probability of t-1，t From state S t-1 Transfer to St The probability of b i (O t ) is in state S i Observed O t probability.

[0106] By continuously updating α t (S i ), the system can track the state changes of the equipment in real time and make dynamic adjustments based on the observation data provided by the sensor to ensure that the observation probability can accurately reflect the current operating status of the equipment.

[0107] Long Short-Term Memory (LSTM) Unit

[0108] In this embodiment, the fault prediction module also includes a long short-term memory network (LSTM) unit, which is used to perform trend analysis on the long-term status of the equipment.

[0109] Input data sequence: LSTM takes historical observation data O1, O2, ..., O T As input, O t is the observation data of the device at time t. LSTM extracts the long-term trend of the device's operating status by memorizing and updating the status of these data.

[0110] LSTM unit structure: The core of the LSTM unit includes the input gate, forget gate and output gate, which are used to control the update and output of the device operation data in the memory unit:

[0111] Forget Gate t The calculation formula is:

[0112] f t =σ(W f [h t-1 , O t ]+b f )

[0113] Among them, W f is the weight matrix of the forget gate, h t-1 is the hidden state of the previous moment, O t is the current input, b f is the bias of the forget gate, and σ is the sigmoid activation function.

[0114] Input Gate i t and candidate memory cells The calculation formula is:

[0115] i t =σ(W i [h t-1 , O t ]+bi )

[0116]

[0117] Among them, W i and W c is the weight matrix of the input gate and memory unit, b i and b c are the biases of the input gate and memory unit, respectively.

[0118] Memory unit update: Combine the forget gate and the input gate to update the memory unit C of LSTM t To update, the formula is:

[0119]

[0120] Among them, C t-1 is the memory unit of the previous moment, and ⊙ represents element-level multiplication.

[0121] Output gate o t The calculation formula is:

[0122] o t =σ(W o [h t-1 , O t ]+b o )

[0123] Combined with the current memory unit C t , update the hidden state h t :

[0124] h t =o t ⊙tanh(C t )

[0125] Long-term failure trend prediction: By modeling the dependency relationship of historical data of the equipment, LSTM can predict the long-term failure trend of the equipment. The output hidden state h of LSTM t It represents the operating status of the equipment in the future period of time and can provide operators with long-term prediction reports on the health status of the equipment.

[0126] The fault prediction module in this embodiment predicts the short-term and long-term states of the equipment through hidden Markov models and long short-term memory networks, and can identify potential problems in the operation of the equipment from different time scales. The HMM unit is suitable for modeling the short-term state changes of the equipment, and predicts the short-term failure risk of the equipment through state transition and observation probability matrices. The LSTM unit can capture the long-term trend of the equipment and predict the future state changes of the equipment by modeling long-term dependencies.

[0127] Adaptive Control Module

[0128] The adaptive control module dynamically adjusts the operating parameters of the equipment to ensure that the equipment can maintain stable and efficient operation under different working conditions. This module combines Lyapunov adaptive control and distributed reinforcement learning algorithms to ensure the stability of the equipment and the optimization of the control strategy in a dynamic environment by adaptively adjusting the control parameters. The core task of the adaptive control module is to adjust the control input of the equipment through real-time feedback so that it can adapt to the changing working environment and production needs, thereby ensuring the safety and production efficiency of the equipment.

[0129] In this embodiment, the adaptive control module combines Lyapunov adaptive control and distributed reinforcement learning algorithm to realize dynamic control and parameter optimization of the equipment. The module adjusts the control parameters of the equipment according to the real-time status information and fault prediction results to ensure the stability and optimal control performance of the equipment under various operating conditions.

[0130] Lyapunov Adaptive Control Unit

[0131] In this embodiment, the Lyapunov adaptive control unit is used to ensure the dynamic stability of the device. By constructing the Lyapunov function to evaluate the stability of the system, the system can adjust the control parameters in real time when the device state changes to ensure the stable operation of the device.

[0132] Selection of Lyapunov function: First, select a positive definite Lyapunov function V(x) to describe the energy level of the device state x. The common form of the Lyapunov function is:

[0133] V(x)=x T Px

[0134] Where P is a symmetric positive definite matrix, x is the device state vector, V(x) represents the energy function of the device state, and V(x) should decrease gradually over time to ensure the stability of the system.

[0135] Lyapunov stability condition: In order to ensure the stability of the system, the time derivative of the Lyapunov function V(x) The following conditions must be met:

[0136]

[0137] This means that the energy of the device decreases over time, ensuring the stable operation of the system. The system adjusts the change of the device state by controlling the input u(t) to ensure is negative.

[0138] State feedback control law design: Based on the Lyapunov stability theory, an adaptive control law is designed so that the state of the equipment can be adjusted according to the real-time data fed back by the current sensor. The control law can be expressed as:

[0139] u(t)=-Kx(t)

[0140] Among them, u(t) is the control input, K is the control gain matrix, and the adaptive control of the equipment is achieved by adjusting the K value in real time to ensure stability and optimized performance under different working conditions.

[0141] Adaptive adjustment of control parameters: When the equipment status and working conditions change, the system maintains stability by adjusting the control gain K in real time. The adjustment is based on the derivative of the Lyapunov function. When it approaches zero, increase the control gain K to accelerate the system to return to a stable state; When it is small, the control gain is reduced to reduce energy consumption and maintain stability. Ultimately, the control system ensures:

[0142]

[0143] Among them, Q is a positive definite matrix, which is used to ensure the asymptotic stability of the system.

[0144] Distributed Reinforcement Learning Unit

[0145] In this embodiment, the distributed reinforcement learning unit is used to optimize the control strategy through the reinforcement learning (RL) algorithm in a multi-device environment. Each device independently runs the reinforcement learning algorithm, and through system-level coordination, collaborative optimization control between devices is achieved.

[0146] Definition of state space and action space: The state space S of each device represents all possible states of the device, and the action space A represents the executable control actions. The system optimizes the control strategy of the device by learning the mapping relationship between state and action. Set S = s1, s2, ..., s n is the state set of the device, A=a1,a2,...,a m A collection of actions for the device.

[0147] Design of reward function: In reinforcement learning, define the reward function r t It is used to quantify the effect of a device performing an action. The expression of the reward function is:

[0148] r t =R(s t , a t )

[0149] Among them, st is the state of the device at time t, a t is the control input, R(s t , a t ) represents the impact of the control input on the operation of the equipment. The reward function is defined based on the equipment's operating efficiency, energy consumption or other optimization objectives.

[0150] Optimization of Q-Learning algorithm: The system uses the Q-Learning algorithm to learn the optimal control strategy of the equipment, Q value Q(s t , a t ) means in state s t Next, perform action a t After that, the device obtains the expected cumulative reward. The update formula of Q value is:

[0151] Q(s t , a t )←Q(s t , a t )+α[r t +γmax a Q(s t+1 , a)-Q(s t , a t )]

[0152] Among them, α is the learning rate, which controls the step size of Q value update; γ is the discount factor, which controls the weight of future returns; r t The device is performing action a t The immediate return after max a Q(s t+1 , a) is the device in the next state s t+1 The maximum Q value obtained by selecting the optimal action a.

[0153] ∈-Greedy Strategy: In order to strike a balance between exploration and exploitation, the system adopts the ∈-Greedy Strategy to select actions. The device selects the action with the largest current Q value with probability 1-∈, and randomly selects actions with probability ∈. By gradually reducing ∈, the system eventually tends to select the optimal action.

[0154] Distributed collaborative optimization: In a multi-device environment, each device independently executes a reinforcement learning algorithm, and by sharing device status information, collaborative optimization control between devices is achieved at the system level. Through distributed learning, each device can optimize its own control strategy and coordinate with other devices to improve the overall production efficiency of the factory.

[0155] Monitoring feedback module

[0156] The monitoring feedback module ensures real-time monitoring of industrial equipment through edge computing and cloud collaboration architecture, can quickly process the equipment's operating data, and generate alarm information when the equipment status is abnormal or the fault prediction result is abnormal.

[0157] In this embodiment, the monitoring feedback module realizes real-time monitoring, data analysis and feedback of the equipment through the synergy of the edge computing unit and the cloud optimization unit. This module ensures that any abnormal state can be detected and an early warning can be generated in time during the operation of the equipment. At the same time, the current state of the equipment can be monitored and fed back in real time to adjust the control strategy and ensure the stability of the system operation.

[0158] Edge computing unit

[0159] In this embodiment, the edge computing unit of the monitoring feedback module is used to monitor and respond locally to local devices in real time. The edge computing unit is directly connected to the data acquisition module to obtain device sensor data in real time and process it locally to reduce response time. The specific implementation is as follows:

[0160] Real-time data processing and storage: The edge computing unit obtains real-time data from device sensors and performs preliminary processing on it, including data cleaning, filtering, and anomaly detection. By preprocessing sensor data, the edge computing node can quickly identify changes in device status and store the processed data for subsequent use.

[0161] Local response and control adjustment: The edge computing unit quickly adjusts the operating parameters of the device based on the real-time status information of the device. When it is detected that the operating parameters of the device exceed the set threshold, the system will immediately adjust the control parameters of the device according to the preset response rules to ensure the stable operation of the device.

[0162] Local alarm mechanism: When the edge computing unit detects that the device is in an abnormal state, the system will immediately issue a local alarm message and feedback to the operator. The alarm information includes the current state of the device, detailed information of the abnormal data, and recommended control adjustment measures to help operators respond to the signs of equipment failure in a timely manner.

[0163] Cloud Optimization Unit

[0164] In this embodiment, the cloud optimization unit of the monitoring feedback module is responsible for global monitoring and optimization feedback of the data of the entire factory. The cloud optimization unit not only integrates data from multiple edge computing nodes, but also uses advanced analysis algorithms to collaboratively optimize the operating status of the equipment in the entire factory to ensure that the overall efficiency of the entire factory is maximized.

[0165] Global data collection and analysis: The cloud optimization unit collects processed device status data from the edge computing units of multiple devices and uses big data analysis technology to conduct real-time evaluation of the overall status of factory equipment. The core of data analysis is to identify the collaborative relationship between multiple devices in order to optimize the resource allocation and control strategy of the entire factory.

[0166] Feedback mechanism and strategy update: Based on the cloud-based analysis of the operating status of the entire plant equipment, the cloud-based optimization unit generates a global optimal control strategy and feeds it back to each edge computing node. Each edge computing node adjusts the control strategy of the equipment in real time based on the cloud-based optimization feedback to make it meet the optimal control objectives of the entire factory.

[0167] Feedback mechanism and real-time alarm

[0168] In this embodiment, the monitoring feedback module also includes a complete real-time feedback and alarm mechanism. When the real-time status of the equipment is abnormal or the fault prediction module predicts a potential risk, the system will automatically generate an alarm message and send the alarm message to the remote control center or the operator's terminal through the network.

[0169] Alarm information generation: The system detects potential failure risks during equipment operation through real-time data analysis of the fault prediction module and edge computing unit. Once it predicts that the equipment state is about to enter the dangerous area, the system will generate a detailed alarm report, including the current state of the equipment, relevant sensor data, predicted results of the fault type, and recommended countermeasures.

[0170] Feedback method: Feedback information is transmitted in a variety of ways, including local display, cloud push, and transmission to the remote monitoring system through the API interface. The transmission and display of feedback information has the characteristics of low latency, ensuring that operators can understand the health status of the equipment in the shortest time.

[0171] The present invention realizes comprehensive real-time monitoring and optimization control of multiple industrial equipment through the collaborative work of multi-sensor data acquisition, data processing, fault prediction, adaptive control and monitoring feedback modules. The system effectively removes noise and reduces data redundancy through Kalman filtering and principal component analysis technology to ensure the accuracy and real-time performance of data; the system uses a combination of hidden Markov model and long short-term memory network to accurately predict short-term and long-term faults of equipment and issue fault warnings in time; Lyapunov adaptive control and distributed reinforcement learning are used to realize dynamic control and optimal strategy learning of equipment to ensure the stability and efficient operation of equipment; edge computing and cloud collaboration mechanisms ensure the rapid response and global optimization control of equipment. Through this system, the operating efficiency of factory equipment can be improved, downtime can be reduced, and the maintenance cycle of equipment can be optimized, greatly improving the overall production efficiency of the factory and the intelligent level of equipment management.

[0172] Please see attached Figure 2 The present invention also provides a multi-sensor integration method for remote monitoring of industrial equipment, comprising the following steps:

[0173] S1. Collect multi-dimensional data such as temperature, vibration, pressure and energy consumption from the sensor array of multiple devices to ensure comprehensive acquisition of the operating status information of the equipment;

[0174] S2. Use Kalman filtering to perform noise reduction on the collected data to eliminate random noise in the data, and predict the current state of the device through state estimation;

[0175] S3, use principal component analysis (PCA) to reduce the dimensionality of multi-dimensional sensor data, extract key feature data, and reduce data redundancy;

[0176] S4. Predict short-term equipment failures based on Hidden Markov Model (HMM), and use Long Short-Term Memory Network (LSTM) to predict long-term equipment operation trends and conduct comprehensive fault warnings.

[0177] S5. Based on the fault prediction results, the Lyapunov adaptive control algorithm is used to dynamically adjust the control parameters of the equipment to ensure system stability, while optimizing the control strategy of the equipment through distributed reinforcement learning;

[0178] S6. Through edge computing and cloud collaboration, remote monitoring and real-time feedback of equipment can be achieved, and fault alarm information can be generated when abnormal conditions are detected to ensure safe operation of equipment.

[0179] This method realizes the collection, noise reduction, feature extraction and fault prediction of multi-dimensional equipment data, and remotely monitors and regulates equipment through adaptive control and optimization strategies, effectively improving the operating efficiency and safety of industrial equipment.

[0180] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-sensor integrated system for remote monitoring of industrial equipment, characterized in that: include: Data acquisition module, used to collect multiple sensor data of industrial equipment, including temperature, vibration, pressure and energy consumption parameters; A data processing module, used to perform noise reduction, data fusion and dimension reduction processing on the collected sensor data to generate real-time status information of the device; A fault prediction module, used to predict short-term and long-term faults of equipment based on the real-time status information and provide fault warning information; An adaptive control module, used to dynamically adjust the operating parameters of the equipment according to the real-time status information and the fault prediction result, so as to realize adaptive control of the equipment; The monitoring feedback module is used to monitor the multiple devices in real time and generate alarm information to feedback the device status to the operator.

2. A multi-sensor integrated system for remote monitoring of industrial equipment according to claim 1, characterized in that: The data acquisition module includes a Kalman filter unit, which is used to perform real-time noise filtering and state estimation on sensor data of multiple devices. The Kalman filter unit includes the following steps: State prediction: predict the state of the device based on the state transition equation of the device; Observation prediction: According to the observation equation of the device sensor, predict the observation value of the sensor at time k; Status update: based on the actual observed value z k , combined with the Kalman gain K k , update the status of the device; Covariance update: Update the covariance of the prediction error based on the covariance matrix after the state update.

3. A multi-sensor integrated system for remote monitoring of industrial equipment according to claim 1, characterized in that: The data acquisition module further includes a principal component analysis unit, which is used to perform dimensionality reduction processing on high-dimensional data of multiple device sensors to reduce data redundancy and extract key features. The principal component analysis unit includes the following steps: Data centering: The raw data collected by multiple device sensors are centrally processed, the mean of each sensor is calculated, and the data matrix X is decentralized to obtain in Represents the decentralized data matrix, n is the number of data samples, p is the number of sensors; Calculation of the covariance matrix: based on decentralized data Calculate the covariance matrix C of the data, the formula is: in, is the transpose of the data matrix, is the covariance matrix of p sensor data; Eigendecomposition: Perform eigenvalue decomposition on the covariance matrix C and solve the eigenvalue λ i and the corresponding eigenvector v i , where λ i is the i-th eigenvalue of the covariance matrix C, v i For the corresponding eigenvector, arrange the eigenvectors to form a feature matrix V; Dimensionality reduction: Select the eigenvectors corresponding to the first k largest eigenvalues ​​to form a dimensionality reduction matrix And high-dimensional data The dimension reduction is performed through the matrix V, and the formula is: in, is the data matrix after dimensionality reduction, and k is the number of feature dimensions retained after dimensionality reduction.

4. A multi-sensor integrated system for remote monitoring of industrial equipment according to claim 1, characterized in that: The fault prediction module includes a hidden Markov model unit, which is used to predict short-term faults of equipment and analyze the state changes of equipment based on time series. The hidden Markov model unit includes the following steps: State definition: Set the hidden state set of the device to S1, S2, ..., S N , where S i represents the i-th state that the device may be in, and N is the number of hidden states; State transition probability matrix: Use the state transition matrix A = [a ij ] is used to represent the transition probability of the device between different states, where a ij Indicates that the device status changes from S i Transfer to S j The probability that the following conditions are met: a ij =P(S j |S i ) And the state transfer matrix A satisfies the following conditions: Among them, i,j=1,2,…,N, indicating that the sum of the transition probabilities between all states is 1; Observation probability matrix: Define the observation value set as O1, O2, …, O T , where O t is the device sensor data observed at time t, through the observation probability matrix B = [b j (o)] The computing device is in state S j The probability of observing data o at time , the formula is: b j (o)=P(O t =o|S t =S j ) Among them, b j (o) is in the hidden state S j The probability of observing data o under , where o is the observed value of the device; Initial state probability vector: Define the initial state probability vector π = [π i ],π i Indicates that the device is initially in the hidden state S i The probability of satisfying: Among them, π i =P(S1=S i ) indicates that at time 1 the device is in state S i The probability of The forward algorithm calculates the probability of the device state: The forward algorithm calculates the device in the hidden state S at time t i The probability α t (S i ), the formula is: Among them, α t-1 (S t-1 ) is the state S of the device at time t-1 t-1 The probability of t-1,t From state S t-1 Transfer to S t The probability of b i (O t ) is in state S i Observed O t probability.

5. A multi-sensor integrated system for remote monitoring of industrial equipment according to claim 4, characterized in that: The observation probability calculation mechanism of the hidden Markov model unit includes multi-sensor fusion processing, which calculates the observation probability of the current state in real time based on multiple sensor data of the device.

6. A multi-sensor integrated system for remote monitoring of industrial equipment according to claim 1, characterized in that: The fault prediction module further includes a long short-term memory network unit, which is used to predict the long-term operation trend of the equipment based on the historical sensor data of the equipment. The long short-term memory network unit includes the following steps: Input data definition: The historical observation data sequence O1, O2, ..., O T As the input of the LSTM network, O t is the observation data of the device at time t. The input data sequence is preprocessed according to the time step to retain the temporal characteristics of the data; Long-term trend prediction: By modeling the long-term dependency of historical data from device sensors, the LSTM network can capture the operating trend of the device in the future time period and output h t Generate estimates of the future state of the equipment and identify possible operational anomalies or failures.

7. A multi-sensor integrated system for remote monitoring of industrial equipment according to claim 1, characterized in that: The adaptive control module includes a Lyapunov adaptive control unit, which is used to dynamically adjust the control parameters of the device according to the data fed back in real time by multiple sensors to ensure the stability of the device under different operating conditions. The Lyapunov adaptive control unit includes the following steps: Selection of Lyapunov function: Select a positive Lyapunov function V(x) to analyze the stability of the device system, where x is the state vector of the device. The expression of the Lyapunov function is: V(x)=x T Px Where P is a symmetric positive definite matrix, x is the device state vector, V(x) represents the energy function of the device state, and V(x) should decrease gradually over time to ensure the stability of the system. Lyapunov stability condition: By calculating the time derivative of the Lyapunov function Evaluate the state change trend of the equipment, and the time derivative satisfies: To ensure stable operation of the equipment, Lyapunov stability requires Indicates that the device state energy is non-increasing over time, thus ensuring system stability; Design of state feedback control law: Based on the Lyapunov stability theory, an adaptive control law is designed so that the state x of the device can be adaptively adjusted according to the real-time data fed back by the sensor. The expression of the control law is: u(t)=-Kx(t) Among them, u(t) is the control input, K is the control gain matrix, and the adaptive control of the equipment is achieved by adjusting the K value in real time to ensure stability and optimized performance under different working conditions; Adaptive adjustment of control parameters: When the working condition or load of the equipment changes, the control gain K is adjusted in real time to maintain the stability of the system. The adjustment rule is based on the state change trend of the Lyapunov function, that is, when When it approaches zero, increase the control gain to accelerate the system stability. If it is significantly less than zero, the control gain is reduced to save energy, and finally: Among them, Q is a positive definite matrix, which is used to ensure the asymptotic stability of the system, and the control input u(t) is adaptively adjusted as the state changes.

8. The multi-sensor integrated system for remote monitoring of industrial equipment according to claim 1, characterized in that: The adaptive control module also includes a distributed reinforcement learning unit, which is used to optimize the control strategy of each device through a reinforcement learning algorithm to achieve collaborative optimization control among multiple devices. The working process of the distributed reinforcement learning unit includes the following steps: Definition of state space and action space: Set the state space S and action space A of each device, where S = s1, s2, …, s n is the set of all possible states of the device, A=a1,a2,…,a m is the set of control actions that the device can perform, s t ∈S is the state of the device at time t, s t ∈S is the action selected by the device at time t; Definition of reward function: Based on the equipment's operating efficiency and control objectives, define the reward function r t , used to quantify the time at which the device performs action a t The immediate benefit obtained after the transaction is: r t =R(s t ,a t ) Among them, R(s t ,a t ) is the device in state s t Next, perform action a t The reward obtained, the reward function is defined based on the energy consumption, production efficiency or equipment stability of the equipment; State-action value update of Q-Learning algorithm: The control strategy of the device is learned and optimized through the Q-Learning algorithm. The Q function Q(s t ,a t ) means in state s t Next select action a t The cumulative expected return obtained after that, the Q function update formula is: Q(s t ,a t )←Q(s t ,a t )+α[r t +γmax a Q(s t+1 ,a)-Q(s t ,a t )] Among them, α is the learning rate, which controls the step size of Q value update; γ is the discount factor, which controls the weight of future returns; r t The device is performing action a t The immediate return after max a Q(s t+1 ,a) is the device in the next state s t+1 The maximum Q value obtained by selecting the optimal action a; Strategy selection and update: At each time step t, the device selects and updates the state s according to its current state. t Choose the best action a t , the action selection can be performed through the ∈-greedy strategy, which is defined as: the device randomly selects an action with probability ∈, and selects the optimal action with the largest current Q value with probability 1-∈, ensuring a balance between exploration and exploitation.

9. The multi-sensor integrated system for remote monitoring of industrial equipment according to claim 1, characterized in that: The remote monitoring and feedback module includes an edge computing unit and a cloud optimization unit. The edge computing unit is used to perform real-time processing and local control of local sensor data of the device, and the cloud optimization unit is used to perform global monitoring, device collaborative optimization and control strategy adjustment.

10. A multi-sensor integration method for remote monitoring of industrial equipment, applied to the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collect multi-dimensional data including temperature, vibration, pressure and energy consumption from sensor arrays of multiple devices; Using Kalman filtering to perform noise reduction on the data and predict the current state of the device; Using principal component analysis to reduce the dimensionality of the multi-dimensional data to generate low-dimensional feature data; Predict short-term equipment failures based on hidden Markov models, and predict long-term equipment operating trends based on long short-term memory networks; According to the prediction results, the control parameters of the equipment are adjusted through Lyapunov adaptive control, and the control strategy is optimized through distributed reinforcement learning; Through edge computing and cloud collaboration, remote monitoring and feedback of equipment can be achieved, and fault alarm information can be generated.

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