Digital operation and maintenance management and control system and method for electromechanical facilities

Abstract

The invention relates to the technical field of digital operation and maintenance systems, in particular to a digital operation and maintenance management and control system and method for electromechanical facilities, and the system comprises a vibration data collection module, a data processing module, a vibration characteristic analysis module, a data processing module, a data processing module and a data processing module, receiving the vibration state parameters sent by the data processing module; based on the vibration state parameters, executing a vibration data topological characteristic analysis and prediction system algorithm; generating a prediction result; the operation and maintenance decision module is in communication connection with the vibration feature analysis module and receives the prediction result sent by the vibration feature analysis module; generating operation and maintenance decision information based on the prediction result; the execution control module is in communication connection with the operation and maintenance decision-making module and receives operation and maintenance decision-making information sent by the operation and maintenance decision-making module; the corresponding operation and maintenance control operation is executed based on the operation and maintenance decision information, so that tiny anomalies possibly neglected by a traditional method can be captured, and the accuracy and advance time of fault prediction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital operation and maintenance systems, and in particular to a digital operation and maintenance control system and method for electromechanical facilities. Background Art

[0002] With the advent of the Industrial 4.0 era, digital operation, maintenance and control of electromechanical facilities have become an indispensable part of modern industrial production. Traditional equipment maintenance methods mainly rely on regular inspections and empirical judgments. This method often fails to detect potential faults in a timely manner, which can easily lead to unplanned equipment downtime and huge economic losses. In recent years, with the development of sensor technology and data analysis technology, predictive maintenance has gradually become the focus of the industry.

[0003] Currently, the closest existing technologies mainly include threshold-based monitoring systems and predictive maintenance systems using machine learning. Threshold-based monitoring systems set a series of thresholds for vibration amplitude and frequency, and trigger alarms when the measured values ​​exceed these thresholds. This method is simple to operate and has a fast response speed, but it has obvious limitations. First, it is difficult to capture complex failure modes, especially those that do not have obvious threshold breakthroughs in the early stages. Second, fixed thresholds often cannot adapt to the dynamic changes of equipment under different working conditions, which can easily lead to high false alarm rates or high missed alarm rates.

[0004] Predictive maintenance systems that use machine learning train models with historical data and then classify and predict new data. This method is a significant improvement over traditional threshold methods and can handle more complex data patterns. However, it still has some problems. First, machine learning models are often black-box and lack interpretability, which may be difficult to accept in certain high-risk scenarios. Second, the performance of machine learning models is highly dependent on the quality and quantity of training data. When data is scarce or the data distribution changes, the model performance may drop significantly. Finally, traditional machine learning methods may have difficulty capturing certain complex structures and long-term dependencies in the data, which limits its application in complex systems.

[0005] Given these limitations of existing technologies, the industry urgently needs a more advanced and reliable digital operation and maintenance management system for electromechanical facilities. The ideal system should be able to deeply analyze the operating status of equipment from multiple dimensions, accurately predict potential failures, and have strong adaptability and explainability. Summary of the invention

[0006] The digital operation and maintenance control system and method for electromechanical facilities of the present invention are designed to address the above-mentioned problems. The system of the present invention achieves comprehensive perception, accurate analysis and reliable prediction of the operating status of electromechanical facilities by integrating multidisciplinary knowledge and advanced algorithms.

[0007] The present invention proposes a digital operation and maintenance control system for electromechanical facilities, comprising: Vibration data acquisition module for: Obtain vibration data of electromechanical facilities; Sending the vibration data to a data processing module; A data processing module is connected to the vibration data acquisition module for: Receiving vibration data sent by the vibration data acquisition module; Performing time-frequency domain conversion on the vibration data to obtain a time-domain signal and a frequency-domain signal; Generate a vibration state parameter based on the time domain signal and the frequency domain signal; A vibration characteristic analysis module is connected in communication with the data processing module and is used to: Receiving the vibration state parameters sent by the data processing module; Based on the vibration state parameters, executing vibration data topological feature analysis and prediction system algorithm; Generate prediction results; The operation and maintenance decision module is in communication with the vibration characteristic analysis module and is used to: Receiving the prediction result sent by the vibration characteristic analysis module; Based on the prediction results, generate operation and maintenance decision information; An execution control module is connected in communication with the operation and maintenance decision module and is used to: Receiving operation and maintenance decision information sent by the operation and maintenance decision module; Based on the operation and maintenance decision information, corresponding operation and maintenance control operations are performed.

[0008] Preferably, the vibration data acquisition module comprises: A first sensor is used to obtain vibration data of a support device for mounting an electromechanical facility; A second sensor is used to obtain vibration data of the electromechanical facility body; The data aggregation unit is connected to the first sensor and the second sensor for aggregating the vibration data collected by the first sensor and the second sensor.

[0009] Preferably, the data processing module comprises: A time-frequency conversion unit, used for converting the vibration data into a time-frequency domain; A parameter generating unit, configured to generate a vibration state parameter based on the time domain signal and the frequency domain signal; The vibration state parameters include peak factor, kurtosis index, impact index and root mean square value.

[0010] Preferably, the vibration characteristic analysis module comprises: A topological mapping unit, used for mapping the vibration state parameters to a high-dimensional topological space; A group theory feature extraction unit, used for performing group theory analysis on the features of the high-dimensional topological space; Matrix embedding unit, used to embed group theory features into special matrices and perform spectral analysis; A number theory transformation unit, used for performing number theory transformation and chaos analysis on eigenvalues; The quantum probability prediction unit is used to construct a quantum probability prediction model based on the chaos analysis results.

[0011] Preferably, the topology mapping unit performs the following operations: Preferably, the topology mapping unit performs the following operations: , in, is the topological eigenvector, is the input vibration data set, is the k-dimensional Betti number, which indicates the number of k-dimensional topological features, is the weight coefficient, which is used to adjust the importance of Betti numbers in different dimensions. is the dimension indicator, is the highest dimension considered, Indicates sum.

[0012] Preferably, the group theory feature extraction unit performs the following operations: Computational group theory features , in, For group theory characteristics, is the topological eigenvector, is the multiplication symbol, is the indicator variable, is the dimension of the topological eigenvector, for The symmetry group of is the i-th component of the topological eigenvector, represents the semi-direct product operation, is the direct product of the m-dimensional binary cyclic groups.

[0013] Preferably, the matrix embedding unit performs the following operations: Compute the matrix representation of the group ; Calculate the eigenvalue set , in, is the matrix representation of the group, For group theory characteristics, is the matrix exponential function, It means summation, is an element in group G, is the representation matrix of the group element g, for The set of eigenvalues ​​of is the characteristic value.

[0014] Preferably, the number theory transformation unit performs the following operations: Zeta function to calculate eigenvalues ; Calculate Lyapunov exponent , in, is the Zeta function of the eigenvalue, is the set of eigenvalues, is the multiplication symbol, is the indicator variable, is the number of eigenvalues, is the i-th eigenvalue, is a complex variable, is the Lyapunov exponent, is the limit symbol, For a variable that tends to infinity, is the natural logarithm, is the absolute value symbol, is the i-th order derivative of the Zeta function.

[0015] Preferably, the quantum probability prediction unit performs the following operations: Constructing quantum states ; Calculating probability distributions ; in, , is a quantum state, represents the summation sign, is the indicator variable, is the total number of possible states of the system, is the probability amplitude, is the probability that the measurement result is i, is the i-th ground state of the system, is a natural constant, is the inverse temperature of the system, is the energy eigenvalue, For the summation index, is the Lyapunov index corresponding to the i-th state.

[0016] A digital operation and maintenance control method for electromechanical facilities based on the system includes the following steps: Obtain vibration data of electromechanical facilities; Performing time-frequency domain conversion on the vibration data to obtain a time-domain signal and a frequency-domain signal; Generate a vibration state parameter based on the time domain signal and the frequency domain signal; Execute vibration data topology feature analysis and prediction system algorithms, including: Mapping the vibration state parameters to a high-dimensional topological space; Performing group theory analysis on the characteristics of the high-dimensional topological space; Embed group theory features into special matrices and perform spectral analysis; Perform number-theoretic transformation and chaos analysis on eigenvalues; Construct a quantum probability prediction model based on chaos analysis results; Generate operation and maintenance decision information based on the output results of the quantum probability prediction model; According to the operation and maintenance decision information, corresponding operation and maintenance control operations are performed.

[0017] The beneficial effects of the present invention are mainly reflected in the following aspects: First, the system of the present invention adopts the innovative Vibration Data Topological Feature Analysis and Prediction System (VDTAPS) algorithm, which integrates advanced concepts from multiple mathematical branches such as topology, group theory, and matrix theory. This multi-dimensional data analysis method can capture tiny anomalies that traditional methods may ignore, greatly improving the accuracy and lead time of fault prediction. For example, in actual applications, the system can make accurate warnings 72 hours before a fault occurs, which provides maintenance personnel with sufficient preparation time and effectively reduces the risk of unplanned downtime.

[0018] Secondly, the system of the present invention has strong adaptive capabilities. By introducing the quantum probability prediction model, the system can better process high-dimensional complex data and adapt to the dynamic changes of the equipment under different working conditions. This solves the problem that the traditional fixed threshold method is difficult to adapt to complex working conditions, and significantly reduces the false alarm rate and missed alarm rate. In the test, the false alarm rate of this system was only 2.1%, and the missed alarm rate was only 1.5%, which is much better than the existing technology.

[0019] Third, the system of the present invention also has significant advantages in interpretability. Unlike black-box machine learning models, each step of the VDTAPS algorithm has a clear physical or mathematical meaning. This not only helps users understand and trust the system's decisions, but also provides the possibility for further optimization of the system. This interpretability is particularly important in some high-risk industrial scenarios.

[0020] Fourth, the system of the present invention achieves high flexibility and scalability through modular design. Each functional module communicates through a clear interface and can be flexibly configured and optimized according to specific application requirements. This enables the system to adapt to various types of electromechanical facilities and operating environments, greatly expanding its scope of application.

[0021] Finally, the system of the present invention also performs well in terms of overall performance and efficiency. Despite the use of complex algorithms, the system's response time remains at around 0.5 seconds, fully meeting the needs of real-time monitoring. At the same time, since the system can predict potential failures earlier and more accurately, it can help users optimize maintenance plans and reduce unnecessary inspections and maintenance, thereby significantly reducing operation and maintenance costs and improving the overall efficiency of equipment use.

[0022] In general, the present invention effectively solves the shortcomings of the existing technology in terms of accuracy, adaptability, interpretability and efficiency through innovative algorithm design and system architecture. It can not only significantly improve the reliability and safety of electromechanical facilities, but also help users optimize resource allocation and improve overall operational efficiency. In the context of industrial digital transformation, the present invention provides a comprehensive, reliable and efficient solution for the intelligent operation and maintenance of electromechanical facilities, which has important theoretical significance and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a logic block diagram of the whole system of the present invention.

[0024] Figure 2 It is a logic block diagram of the vibration data acquisition module of the present invention.

[0025] Figure 3 It is a logic block diagram of the data processing module of the present invention.

[0026] Figure 4 It is a logic block diagram of the vibration characteristic analysis module of the present invention. DETAILED DESCRIPTION

[0027] See also Figure 1-4 The present invention provides a digital operation and maintenance control system and method for electromechanical facilities, aiming to improve the operation and maintenance efficiency and reliability of electromechanical facilities. The following is a detailed description of the system of the present invention: The digital operation and maintenance control system of the present invention comprises a vibration data acquisition module 1, a data processing module 2, a vibration feature analysis module 3, an operation and maintenance decision module 4 and an execution control module 5. These modules work together to achieve a complete closed loop from data acquisition to decision execution.

[0028] The vibration data acquisition module 1 is the data input end of the system, which is used to obtain the vibration data of electromechanical facilities. Preferably, the module can collect a variety of vibration parameters, such as displacement, velocity and acceleration. In one embodiment of the present invention, the vibration data acquisition module 1 adopts a high-precision piezoelectric acceleration sensor with a sampling frequency of up to 20kHz to ensure that high-frequency vibration signals are captured. After acquiring the data, the vibration data acquisition module 1 will first perform a data validity check. This step is to ensure the quality of the collected data and avoid the influence of erroneous data caused by sensor failure, signal interference and other reasons on subsequent analysis. Specifically, the system will check the integrity of the data, whether it is within the expected range, whether there are abnormal fluctuations, etc. If the data is judged to be invalid, the system will re-collect the data; if the data is valid, it will be transmitted to the data processing module 2 for the next step of processing.

[0029] The data acquired by the vibration data acquisition module 1 is then sent to the data processing module 2. The data processing module 2 is the core processing unit of the system, which is responsible for the preliminary processing and analysis of the original vibration data. Specifically, the module first converts the vibration data into the time-frequency domain to obtain the time domain signal and the frequency domain signal. The time-frequency domain conversion usually adopts the fast Fourier transform (FFT) algorithm. The present invention preferably adopts the improved wavelet packet transform algorithm, which can better process non-stationary signals.

[0030] Based on the obtained time domain signal and frequency domain signal, the data processing module 2 further generates vibration state parameters. These parameters usually include statistical characteristics such as root mean square value, peak factor, kurtosis, margin, and characteristic frequency components in the spectrum. For example, for bearing fault diagnosis, special attention may be paid to the characteristic frequency and its harmonics of the bearing.

[0031] The vibration state parameters generated by the data processing module 2 are then sent to the vibration feature analysis module 3. This module is the core innovative part of the present invention, and implements a set of innovative algorithms called Vibration Data Topological Analysis and Prediction System (VDTAPS), which aims to extract deeper features from vibration data and perform predictive analysis.

[0032] The first step of the VDTAPS algorithm is to map the vibrational state parameters into a high-dimensional topological space. This step uses the theory of persistent homology to capture the essential features in the data.

[0033] Next, the algorithm uses group theory methods to extract deeper algebraic features. This step can effectively capture the symmetries and invariances in the vibration data, which is very useful for identifying certain specific types of mechanical failures.

[0034] Subsequent steps of the VDTAPS algorithm include matrix embedding and spectral analysis, number theory transformation and chaos analysis, and the construction of a quantum probability prediction model. These steps further extract the deep features of the vibration data and ultimately generate prediction results.

[0035] Based on the prediction results generated by the vibration feature analysis module 3, the operation and maintenance decision module 4 will generate corresponding operation and maintenance decision information. These decision information may include equipment health status assessment, fault warning, maintenance suggestions, etc. For example, if the prediction results show that a certain bearing has an 80% probability of failure in the next 100 hours, the operation and maintenance decision module 4 may generate a decision to recommend replacing the bearing at the next planned shutdown. After generating the decision information, the operation and maintenance decision module 4 will make a key judgment: whether the electromechanical facilities need to be maintained. This judgment is based on multiple factors, including but not limited to the predicted failure probability, the severity of potential failures, the importance of the equipment, maintenance costs, etc. If the system determines that maintenance is required, it will activate the execution control module 5 and start the corresponding maintenance process. If it is determined that maintenance is not currently required, the system will continue to monitor the equipment status and restart the data collection process. This dynamic decision-making mechanism can maximize the use efficiency of the equipment while ensuring the reliable operation of the equipment and avoid unnecessary maintenance costs.

[0036] Finally, the execution control module 5 performs corresponding operation and maintenance control operations based on the operation and maintenance decision information. This may include automatically adjusting equipment parameters, sending maintenance instructions, triggering alarm systems, etc. In some cases, if signs of serious failure are detected, the execution control module 5 may trigger an emergency shutdown procedure to prevent greater losses.

[0037] The system of the present invention also includes some optimized designs. For example, the vibration data acquisition module 1 includes a first sensor 11 and a second sensor 12. The first sensor 11 is used to obtain vibration data of the electromechanical facility installation support device, while the second sensor 12 is used to obtain vibration data of the electromechanical facility body. This dual sensing design can more comprehensively capture the vibration state of the electromechanical facility and help distinguish between vibrations from the device itself and the external environment.

[0038] The data aggregation unit 13 is in communication with the first sensor 11 and the second sensor 12, and is used to aggregate the vibration data collected by the two sensors. The data aggregation unit 13 uses an advanced data fusion algorithm, which can effectively integrate vibration data from different sources and improve the reliability and integrity of the data.

[0039] The data processing module 2 includes a time-frequency conversion unit 21 and a parameter generation unit 22. The time-frequency conversion unit 21 is responsible for converting the vibration data into the time-frequency domain, while the parameter generation unit 22 generates vibration state parameters based on the time domain signal and the frequency domain signal. This modular design improves the flexibility and maintainability of the system.

[0040] The vibration state parameters generated by the parameter generation unit 22 include peak factor, kurtosis index, impact index and root mean square value. The selection of these parameters is based on the experience and theory of mechanical and electrical equipment fault diagnosis. For example, the kurtosis index is particularly effective for detecting early bearing faults, while the impact index can reflect the gear meshing condition.

[0041] The system of the present invention realizes the intelligent and precise operation and maintenance of electromechanical facilities by integrating multidisciplinary knowledge and advanced algorithms. It can not only detect potential faults in a timely manner, but also predict the future status of the equipment, providing a solid foundation for preventive maintenance. This method can significantly reduce the unplanned downtime of equipment, improve production efficiency, reduce maintenance costs, and extend the life of equipment.

[0042] The vibration feature analysis module 3 of the present invention is the core innovative part of the system, including a topological mapping unit 31, a group theory feature extraction unit 32, a matrix embedding unit 33, a number theory transformation unit 34 and a quantum probability prediction unit 35. These units together constitute a complete implementation of the vibration data topological feature analysis and prediction system (VDTAPS) algorithm.

[0043] In electromechanical facilities, vibration data often contain complex nonlinear structures. With persistent coherence, it is possible to capture the invariant features in these structures, i.e., the geometric shape changes at different time scales. For example, sensors mounted on the blades of a wind turbine collect a large amount of data that fluctuates over time and with environmental changes. Persistent coherence can help identify which fluctuations are caused by equipment aging or potential failures, rather than by external factors such as wind speed changes.

[0044] The topological mapping unit 31 is responsible for mapping the vibration state parameters to a high-dimensional topological space. The core of this step is to calculate the topological feature vector of the vibration data. Specifically, the topological mapping unit 31 performs the following operations: , In this formula is a topological eigenvector, which represents a high-dimensional vector extracted from vibration data that can reflect the structural characteristics of the system. is the input vibration data set, is the k-dimensional Betti number, which indicates the number of k-dimensional topological features, is the weight coefficient, which is used to adjust the importance of Betti numbers in different dimensions. is the dimension indicator, is the highest dimension considered, In a preferred embodiment of the present invention, A value of 3 is usually chosen, which captures the key topological features of most electromechanical systems. The choice of can be adjusted according to the specific application scenario. For example, for rotating machinery, more attention may be paid to the topological characteristics of 1D and 2D, so and The value of .

[0045] This method can effectively distinguish between noise and meaningful information, thereby improving the accuracy of fault detection. It is particularly useful for early detection of subtle anomalies, because even small changes may indicate an impending failure.

[0046] The vibration data acquisition module 1 collects high-frequency vibration signals from multiple locations on the wind turbine blades. These data are sent to the data processing module 2 for preliminary processing, including denoising, standardization, etc. Then, the time-frequency conversion unit 21 converts the time domain signal into a frequency domain signal, and the parameter generation unit 22 calculates various statistical features. Finally, these features are passed as input to the topological mapping unit 31, which uses the above formula to calculate the topological feature vector for subsequent analysis.

[0047] The normal operation of electromechanical equipment is usually accompanied by certain periodicities and symmetries. For example, the rotational motion of a gear system produces a regular vibration pattern. If this pattern changes, such as asymmetry or additional frequency components, it may be caused by gear wear or other mechanical problems. Through group theory analysis, these changes in symmetry can be quantified and used as an indicator for fault diagnosis.

[0048] The group theory feature extraction unit 32 further extracts the number feature based on the topological feature. The core operation of this step is as follows: , In this formula, For group theory characteristics, is the topological eigenvector, is the multiplication symbol, is the indicator variable, is the dimension of the topological eigenvector, for The symmetry group of is the i-th component of the topological eigenvector, It represents the semi-direct product operation, which is a way of combining two groups, where one group acts on the other to form a new group. It is used here to combine different symmetry groups to enhance the expressive power of features. is the direct product of the m-dimensional binary cyclic group, consisting of m The semi-direct product of the modulo-2 additive group represents a discrete symmetry operation that can be understood as a flip or inversion operation. This group theory analysis method can effectively capture the symmetry and invariance in vibration data, which is very useful for identifying certain types of mechanical faults. For example, in a gear system, the vibration data during normal operation usually has a periodic symmetric structure, which may be destroyed when a fault occurs. m is the dimension of the topological eigenvector, that is, the number of symmetry groups involved in the semi-direct product operation, corresponding to the number of vibration state parameters.

[0049] This approach helps identify subtle changes that are not easily noticeable but are critical to device performance. Especially in large and complex systems, traditional threshold-based methods may not be able to capture these problems in time, while group theory features provide a more sensitive and precise tool.

[0050] The vibration data collected from sensors at different positions in the gearbox are preprocessed and used to construct vibration state parameters. The group theory feature extraction unit 32 uses these parameters to calculate the group theory features. By comparing the difference in group theory features under normal state and current state, the health status of the equipment can be evaluated and possible fault points can be predicted.

[0051] The vibration characteristics in electromechanical systems can be expressed in matrix form. For example, an unbalanced motor rotor will cause specific vibration modes, which can be represented by a set of complex matrices. By performing spectral analysis on these matrices, the dynamic behavior of the system, such as resonant frequency, damping ratio, etc., can be understood. In addition, the eigenvalues ​​also provide important information about the stability and response speed of the system.

[0052] The matrix embedding unit 33 embeds the group theory features into a special matrix and performs spectral analysis. This step includes two main operations: First, compute the matrix representation of the group: , Next, calculate the eigenvalue set: , In these formulas, is the matrix representation of the group. The group elements are mapped to the matrix space through linear transformation, so that the group operation can be expressed by matrix multiplication. The characteristics of group theory are A homomorphism to a group of matrices that maps each element of the group to a corresponding matrix, preserving the algebraic structure of the group. is the matrix exponential function. For a matrix A, Defined as , which is an infinite series that, when applied to a matrix, produces a new matrix. It means summation, is an element in group G, is the representation matrix of the group element g, for The set of eigenvalues ​​of is the eigenvalue, which is the matrix The intrinsic values ​​of the matrix determine the main behaviors of the matrix, such as scaling, rotation, etc. is the number of eigenvalues, which is equal to the order of the matrix, that is, the number of rows or columns of the matrix. This matrix embedding method can transform complex group theory features into computable numerical features for subsequent analysis and processing.

[0053] Through matrix embedding and spectral analysis, we can not only gain a deeper understanding, but also develop efficient control strategies. For example, when designing an active vibration reduction control system, it is very important to understand the natural frequencies and modes of the system, which helps to select appropriate controller parameters to achieve the best vibration reduction effect.

[0054] During the operation of the motor, the interaction between the stator and the rotor generates complex electromagnetic and mechanical forces, which cause vibrations. The vibration data acquisition module 1 is responsible for monitoring these vibrations and transmitting the data to the data processing module 2. After a series of processing steps, including time-frequency conversion and feature extraction, the obtained vibration state parameters are used as input to the matrix embedding unit 33. This unit calculates the matrix representation and its eigenvalues ​​to provide a basis for subsequent fault diagnosis and preventive maintenance.

[0055] Electromechanical systems can sometimes exhibit chaotic behavior, especially under high loads or extreme operating conditions. For example, when a hydraulic pump is operating under high pressure, the flow of fluid inside it may become unstable, producing irregular vibrations. By introducing the Zeta function and calculating the Lyapunov exponent, it is possible to quantify the degree of this chaos and predict whether the system will enter an unstable state.

[0056] The number theory transformation unit 34 performs number theory transformation and chaos analysis on the eigenvalue. This step first calculates the Zeta function of the eigenvalue: , Then, calculate the Lyapunov exponent: , In these formulas, is the Zeta function of the eigenvalue, which is a complex variable function used to study the distribution law of eigenvalues, especially the behavior at infinity. is the set of eigenvalues, is the multiplication symbol, is the indicator variable, is the number of eigenvalues, is the i-th eigenvalue, is a complex variable, is the Lyapunov index, which measures the stability of the system over time. A positive value indicates that the system tends to be unstable, while a negative value indicates stability. is the limit symbol, For a variable that tends to infinity, is the natural logarithm, which is used to calculate the logarithm of the absolute value of the derivative. This is to ensure that even if the derivative is negative, a positive value can be obtained, so as not to affect the positive and negative judgment of the Lyapunov exponent. is the absolute value symbol, is the i-th order derivative of the Zeta function. The derivative describes the speed of the Zeta function change, while the higher-order derivative provides more detailed change information. The introduction of the Zeta function allows the distribution of eigenvalues ​​to be studied from the perspective of number theory, while the Lyapunov index reflects the degree of chaos in the system. In electromechanical systems, a sudden increase in the Lyapunov index may mean that the system is about to enter an unstable state.

[0057] This is crucial for taking measures in advance to prevent serious failures. For example, when the Lyapunov exponent suddenly increases, it means that the system is approaching a critical point. At this time, the early warning mechanism should be activated immediately to notify the operator to check the equipment to avoid further deterioration.

[0058] The pressure sensor and flow sensor inside the hydraulic pump continuously monitor the operating status of the system. These data are integrated into the vibration characteristic analysis module 3, in which the number theory transformation unit 34 uses the Zeta function and Lyapunov exponent to evaluate the stability of the system. Once an abnormal situation is detected, the operation and maintenance decision module 4 will generate corresponding maintenance suggestions based on the analysis results, such as adjusting working parameters or arranging emergency maintenance.

[0059] In some cases, the future behavior of electromechanical systems is highly uncertain, and traditional statistical models are difficult to accurately predict. The use of quantum probabilistic prediction models can simulate this uncertainty and provide a probability distribution that is closer to the actual situation. For example, for the safety assessment of nuclear power plants, it is necessary to take into account the influence of various uncertain factors, such as material aging, environmental changes, etc. Quantum probabilistic models can help engineers better understand and manage these risks.

[0060] Finally, the quantum probability prediction unit 35 constructs a quantum probability prediction model based on the chaos analysis results. This step first constructs the quantum state: , Then calculate the probability distribution: , in, , is a quantum state, represents the summation sign, is the indicator variable, is the total number of possible states of the system, is the probability amplitude, and the square of the probability amplitude gives the probability that the measurement result is the state. is the probability of the measurement result being i, calculated according to the Boltzmann distribution, reflecting the possibility that the system is in a specific state, is the i-th basis state of the system, representing a basic state of the system. All possible states are linear combinations of these basis states. is a natural constant, is the inverse temperature of the system, which is the thermodynamic temperature The reciprocal of ,in is the Boltzmann constant, is the energy eigenvalue, For the summation index, is the Lyapunov index corresponding to the i-th state.

[0061] This quantum probability-based prediction model has the advantage of processing high-dimensional complex data and can capture subtle correlations that traditional probability models may ignore. In the operation and maintenance of electromechanical facilities, this model can provide more accurate failure predictions and remaining service life estimates.

[0062] The advantage of this model is that it can handle high-dimensional complex data and capture subtle associations that traditional probability models may ignore. This makes it very suitable for long-term prediction and remaining service life estimation, providing strong support for the formulation of scientific and reasonable maintenance plans.

[0063] Various sensors in the nuclear power plant (such as temperature, pressure, radiation level, etc.) continuously collect data, which are pre-processed to form vibration state parameters. The quantum probability prediction unit 35 uses these parameters to construct the quantum state and predict future failure risks by calculating the probability distribution. The operation and maintenance decision module 4 can optimize the maintenance strategy based on the prediction results to ensure the safe and stable operation of the nuclear power plant.

[0064] The VDTAPS algorithm of the present invention achieves in-depth mining and prediction of vibration data of electromechanical facilities through a series of complex mathematical transformations and analyses. Compared with traditional methods, VDTAPS can better process nonlinear and non-stationary vibration signals and capture minor anomalies that may be overlooked, thereby improving the accuracy of fault diagnosis and prediction.

[0065] In practical applications, the various parameters of the VDTAPS algorithm can be optimized according to the specific type of electromechanical facilities and the operating environment. For example, for high-speed rotating equipment, it may be necessary to increase the weight of high-frequency vibration components; while for large static equipment, more attention may be paid to low-frequency vibration characteristics. Through this flexible parameter adjustment, the system of the present invention can adapt to various industrial scenarios and provide accurate operation and maintenance support for various electromechanical facilities.

[0066] The present invention also provides a digital operation and maintenance control method for electromechanical facilities corresponding to the above system. The method is a further elaboration and refinement of the system function, aiming to provide an electromechanical facility operation and maintenance solution with strong operability and significant effect.

[0067] First, the method starts by acquiring vibration data of electromechanical facilities. Preferably, this step can use a high-precision vibration sensor, such as a piezoelectric accelerometer or an optical fiber sensor. In one embodiment of the present invention, the acquisition frequency of vibration data can reach 20kHz to ensure that high-frequency vibration signals are captured. At the same time, in order to fully reflect the operating status of electromechanical facilities, the method can also simultaneously collect auxiliary parameters such as temperature, pressure, and current.

[0068] After obtaining the vibration data, the method performs a time-frequency domain transformation on the data to obtain a time domain signal and a frequency domain signal. This step usually uses a fast Fourier transform (FFT) algorithm, but in a preferred embodiment of the present invention, an improved wavelet packet transform algorithm is used. Compared with the traditional FFT, the wavelet packet transform has obvious advantages in processing non-stationary signals and can better capture transient characteristics and local characteristics.

[0069] Next, based on the obtained time domain signal and frequency domain signal, the method generates vibration state parameters. These parameters usually include time domain statistical features (such as root mean square value, peak factor, kurtosis, margin, etc.) and frequency domain features (such as characteristic frequency and its amplitude). In one embodiment of the present invention, envelope spectrum analysis is also introduced, which is particularly effective for diagnosing early faults of components such as bearings and gears.

[0070] After generating the vibration state parameters, the method executes the Vibration Data Topology Feature Analysis and Prediction System (VDTAPS) algorithm, which is the core innovative step of the present invention and includes a series of complex mathematical transformations and analysis processes.

[0071] The VDTAPS algorithm first maps the vibration state parameters to a high-dimensional topological space. This step uses the theory of persistent homology to capture the essential topological features in the data. For example, for bearing faults, different types of faults (such as inner race faults, outer race faults, and rolling element faults) may present different characteristic structures in the topological space.

[0072] The algorithm then applies group-theoretic analysis to the features of the high-dimensional topological space. This step is effective in capturing symmetries and invariances in the vibration data. In practice, this is very useful for identifying certain types of mechanical faults. For example, a gear system usually exhibits a periodic symmetric structure when it is operating normally, but this symmetry may be destroyed when a fault occurs.

[0073] Next, the algorithm embeds the group theory features into a special matrix and performs spectral analysis. This step converts the complex group theory features into computable numerical features, which facilitates subsequent analysis and processing. The distribution and changes of the eigenvalues ​​can reflect the overall dynamic characteristics of the electromechanical system.

[0074] Then, the algorithm performs number-theoretic transformation and chaos analysis on the eigenvalues. This step introduces the Zeta function and Lyapunov index, which can deeply analyze the dynamic behavior of the system from a mathematical perspective. In particular, the Lyapunov index can effectively quantify the degree of chaos in the system, which is of great significance for predicting the long-term behavior of the system.

[0075] Finally, the algorithm builds a quantum probability prediction model based on the results of chaos analysis. This prediction model based on the principles of quantum mechanics can better handle high-dimensional complex data and capture subtle correlations compared to traditional probability models. In the operation and maintenance of electromechanical facilities, this model can provide more accurate fault prediction and remaining service life estimation.

[0076] Based on the output of the VDTAPS algorithm, this method generates operation and maintenance decision information. This information may include equipment health status assessment, fault warning, maintenance suggestions, etc. For example, if the prediction model shows that a key component has an 80% probability of failure in the next 100 hours, the system may generate a decision to recommend replacing the component at the next planned downtime.

[0077] Finally, the method performs corresponding operation and maintenance control operations based on the operation and maintenance decision information. This may include automatically adjusting equipment parameters, sending maintenance instructions, triggering alarm systems, etc. In some cases, if signs of serious failure are detected, the system may trigger an emergency shutdown procedure to prevent greater losses.

[0078] The method of the present invention realizes the intelligent and precise operation and maintenance of electromechanical facilities by integrating multidisciplinary knowledge and advanced algorithms. It can not only detect potential faults in a timely manner, but also predict the future status of the equipment, providing a solid foundation for preventive maintenance. This method can significantly reduce the unplanned downtime of equipment, improve production efficiency, reduce maintenance costs, and extend the life of equipment.

[0079] In practical applications, the various steps of this method can be optimized and adjusted according to the specific type of electromechanical facilities and the operating environment. For example, for high-speed rotating equipment, it may be necessary to increase the weight of high-frequency vibration components; while for large static equipment, more attention may be paid to low-frequency vibration characteristics. Through this flexible parameter adjustment, the method of the present invention can adapt to various industrial scenarios and provide accurate operation and maintenance support for various electromechanical facilities.

[0080] In order to verify the superiority of the present invention, a typical industrial scenario was selected for example testing and comparative analysis. Specifically, a centrifugal compressor unit, a key rotating equipment in a large petrochemical plant, was selected as the test object. The reliability of such equipment directly affects the stability and efficiency of the entire production line, so it is of great significance to accurately control its operation and maintenance.

[0081] Example 1: Digital operation and maintenance control system and method using the present invention In this embodiment, a high-precision vibration sensor is installed on the centrifugal compressor unit, and the sampling frequency is set to 20kHz. The system uses the VDTAPS algorithm of the present invention for data analysis and prediction. In particular, the parameters in the algorithm are optimized: in the topology mapping unit, the highest dimension d is set to 3, and the weight coefficient is adjusted according to the characteristics of the compressor. ; In the quantum probability prediction unit, the inverse temperature β of the system is adjusted according to historical data.

[0082] Comparative Example 1: Traditional Threshold-Based Monitoring System This is a method commonly used in the industry. The system sets a series of thresholds for vibration amplitude and frequency, and triggers an alarm when the measured value exceeds these thresholds.

[0083] Comparative Example 2: Predictive Maintenance System Using Machine Learning The system uses a support vector machine (SVM) algorithm to classify and predict equipment status. It trains the model with historical data, then classifies new data and predicts possible failures.

[0084] The three methods were tested over a six-month period, focusing on the following indicators: 1. Fault prediction accuracy: The percentage of correctly predicted faults to the total number of predictions.

[0085] 2. Prediction lead time: How long before the actual failure occurs can the system predict the failure.

[0086] 3. False alarm rate: The percentage of incorrectly reported faults to the total number of reports.

[0087] 4. Missed alarm rate: The percentage of actual faults that were not detected in the total number of faults.

[0088] 5. System response time: the time required from data input to generating prediction results.

[0089] The testing methods and standards are as follows: 1. Fault prediction accuracy: calculated by comparing the system prediction results with the actual faults that occurred.

[0090] 2. Prediction lead time: record the time when the system predicts the failure and the time when the actual failure occurs, and calculate the time difference between the two.

[0091] 3. False alarm rate: The number of times the system reports a fault but no fault actually occurs, divided by the total number of reports.

[0092] 4. Missed alarm rate: record the number of faults that actually occurred but were not detected by the system, divided by the total number of faults.

[0093] 5. System response time: Use a high-precision timer to measure the time from data input to output results.

[0094] The test results are shown in the following table: It can be seen from the test results that the system of the present invention has shown obvious advantages in multiple key indicators. In particular, in terms of the two indicators of fault prediction accuracy and prediction lead time, the performance of the present invention far exceeds that of the other two methods. This means that by using the system of the present invention, the factory can predict potential faults earlier and more accurately, so that there is more time to carry out maintenance planning and avoid sudden downtime.

[0095] The system of the present invention also performs well in terms of false alarm rate and missed alarm rate. Low false alarm rate means that unnecessary inspection and maintenance are reduced, which can save a lot of manpower and material resources. Low missed alarm rate ensures that the system can capture almost all potential faults, greatly improving the reliability and safety of the equipment.

[0096] Although the system response time of the present invention is slightly inferior to the traditional method, it is still within an acceptable range and much faster than the machine learning method. Considering that the prediction lead time provided by the system of the present invention is much greater than that of other methods, the difference in response time can be ignored.

[0097] These excellent test results are mainly due to the VDTAPS algorithm of the present invention. By integrating advanced concepts from multiple mathematical branches such as topology, group theory, and matrix theory, the algorithm is able to extract deeper features from vibration data. In particular, it is able to capture subtle anomalies that may be overlooked by traditional methods and general machine learning methods, which are often important indicators of early failures.

[0098] In addition, the quantum probability prediction model in the VDTAPS algorithm demonstrates strong predictive capabilities. It can not only accurately predict the occurrence of failures, but also provide a long prediction lead time. This provides the factory's maintenance personnel with sufficient preparation time, allowing them to perform maintenance at the most appropriate time and minimize the impact on production.

[0099] In general, these test results fully demonstrate the superiority of the present invention in the field of digital operation and maintenance control of electromechanical facilities. It can not only improve the reliability and safety of equipment, but also help factories optimize maintenance strategies, improve production efficiency, and reduce operation and maintenance costs. In the current context of Industry 4.0 and smart manufacturing, the present invention provides a powerful solution for the intelligent operation and maintenance of electromechanical equipment.

[0100] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital operation and maintenance control system for electromechanical facilities, characterized in that: include: Vibration data acquisition module for: Obtain vibration data of electromechanical facilities; Sending the vibration data to a data processing module; A data processing module is connected to the vibration data acquisition module for: Receiving vibration data sent by the vibration data acquisition module; Performing time-frequency domain conversion on the vibration data to obtain a time-domain signal and a frequency-domain signal; Generate a vibration state parameter based on the time domain signal and the frequency domain signal; A vibration characteristic analysis module is connected in communication with the data processing module and is used to: Receiving the vibration state parameters sent by the data processing module; Based on the vibration state parameters, executing vibration data topological feature analysis and prediction system algorithm; Generate prediction results; The operation and maintenance decision module is in communication with the vibration characteristic analysis module and is used to: Receiving the prediction result sent by the vibration characteristic analysis module; Based on the prediction results, generate operation and maintenance decision information; An execution control module is connected in communication with the operation and maintenance decision module and is used to: Receiving operation and maintenance decision information sent by the operation and maintenance decision module; Based on the operation and maintenance decision information, corresponding operation and maintenance control operations are performed.

2. The system according to claim 1, characterized in that The vibration data acquisition module comprises: A first sensor is used to obtain vibration data of a support device for mounting an electromechanical facility; A second sensor is used to obtain vibration data of the electromechanical facility body; The data aggregation unit is connected to the first sensor and the second sensor for aggregating the vibration data collected by the first sensor and the second sensor.

3. The system according to claim 1, characterized in that The data processing module comprises: A time-frequency conversion unit, used for converting the vibration data into a time-frequency domain; A parameter generating unit, configured to generate a vibration state parameter based on the time domain signal and the frequency domain signal; The vibration state parameters include peak factor, kurtosis index, impact index and root mean square value.

4. The system according to claim 1, characterized in that The vibration characteristic analysis module comprises: A topological mapping unit, used for mapping the vibration state parameters to a high-dimensional topological space; A group theory feature extraction unit, used for performing group theory analysis on the features of the high-dimensional topological space; Matrix embedding unit, used to embed group theory features into special matrices and perform spectral analysis; A number theory transformation unit, used for performing number theory transformation and chaos analysis on eigenvalues; The quantum probability prediction unit is used to construct a quantum probability prediction model based on the chaos analysis results.

5. The system according to claim 4, characterized in that The topology mapping unit performs the following operations: , in, is the topological eigenvector, is the input vibration data set, is the k-dimensional Betti number, which indicates the number of k-dimensional topological features, is the weight coefficient, which is used to adjust the importance of Betti numbers in different dimensions. is the dimension indicator, is the highest dimension considered, Indicates sum.

6. The system according to claim 4, characterized in that The group theory feature extraction unit performs the following operations: Computational group theory features , in, For group theory characteristics, is the topological eigenvector, is the multiplication symbol, is the indicator variable, is the dimension of the topological eigenvector, for The symmetry group of is the i-th component of the topological eigenvector, represents the semi-direct product operation, is the direct product of the m-dimensional binary cyclic groups.

7. The system according to claim 4, characterized in that The matrix embedding unit performs the following operations: Compute the matrix representation of the group ; Calculate the eigenvalue set , in, is the matrix representation of the group, For group theory characteristics, is the matrix exponential function, It means summation, is an element in group G, is the representation matrix of the group element g, for The set of eigenvalues ​​of is the characteristic value.

8. The system according to claim 4, characterized in that The number theory transformation unit performs the following operations: Zeta function to calculate eigenvalues ; Calculate Lyapunov exponent , in, is the Zeta function of the eigenvalue, is the set of eigenvalues, is the multiplication symbol, is the indicator variable, is the number of eigenvalues, is the i-th eigenvalue, is a complex variable, is the Lyapunov exponent, is the limit symbol, For a variable that tends to infinity, is the natural logarithm, is the absolute value symbol, is the i-th order derivative of the Zeta function.

9. The system according to claim 4, characterized in that The quantum probability prediction unit performs the following operations: Constructing quantum states ; Calculating probability distributions ; in, , is a quantum state, represents the summation sign, is the indicator variable, is the total number of possible states of the system, is the probability amplitude, is the probability that the measurement result is i, is the i-th ground state of the system, is a natural constant, is the inverse temperature of the system, is the energy eigenvalue, For the summation index, is the Lyapunov index corresponding to the i-th state.

10. A digital operation and maintenance control method for electromechanical facilities based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Obtain vibration data of electromechanical facilities; Performing time-frequency domain conversion on the vibration data to obtain a time-domain signal and a frequency-domain signal; Generate a vibration state parameter based on the time domain signal and the frequency domain signal; Execute vibration data topology feature analysis and prediction system algorithms, including: Mapping the vibration state parameters to a high-dimensional topological space; Performing group theory analysis on the characteristics of the high-dimensional topological space; Embed group theory features into special matrices and perform spectral analysis; Perform number-theoretic transformation and chaos analysis on eigenvalues; Construct a quantum probability prediction model based on chaos analysis results; Generate operation and maintenance decision information based on the output results of the quantum probability prediction model; According to the operation and maintenance decision information, corresponding operation and maintenance control operations are performed.

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