Quantum intelligent computing and big data fusion industrial equipment fault early warning method and platform

Abstract

The application belongs to the field of big data processing and industrial intelligence, and particularly relates to an industrial equipment fault early warning method and platform based on quantum intelligent computing and big data, which comprises the following steps: collecting and preprocessing multi-source heterogeneous industrial data; implementing quantum state mapping based on amplitude coding to convert characteristic data into superposed states of quantum bits; constructing a parameterized quantum evolution line composed of quantum entanglement layers to extract fault characteristics; updating parameters through quantum measurement and classical gradient optimization; and outputting multi-dimensional fault early warning decisions. The platform is composed of physical perception, heterogeneous computing engine and visual decision modules. The application utilizes quantum parallel processing capability to solve the dimension disaster problem in traditional industrial data processing, and significantly improves the accuracy and real-time performance of equipment fault early warning.

Description

Technical Field

[0001] This invention belongs to the field of big data processing and industrial intelligence. Specifically, it is a method and platform for early warning of industrial equipment faults that integrates quantum intelligent computing with big data. Background Technology

[0002] As global manufacturing transforms towards Industry 4.0, industrial production systems are evolving towards greater integration, precision, and continuity. Under the modern industrial internet architecture, real-time acquisition of equipment physical parameters based on multi-source sensor arrays (such as vibration, temperature, and pressure sensors) and the construction of data-driven health status assessment models have become the mainstream technical solutions for realizing early warning and predictive maintenance of industrial equipment faults. Existing technologies typically employ classical machine learning algorithms or deep neural networks, utilizing convolutional neural networks to extract the spectral features of sensor signals, or recurrent neural networks and their variants to capture the time-series features of equipment performance degradation, thereby identifying potential abnormal signs in the time or frequency domains and achieving a leap from post-event maintenance to pre-event early warning.

[0003] However, as industrial applications increasingly demand higher accuracy, real-time performance, and adaptability to complex operating conditions in early warning systems, the limitations of the aforementioned technical solutions based on classical computing frameworks at the principle level are becoming increasingly apparent. Modern industrial systems exhibit complex physical coupling relationships between internal components, and their data displays extremely high dimensionality, nonlinearity, and strong time-varying characteristics. When the scale of monitoring points and the dimensionality of feature parameters grow exponentially, classical computing architectures fall into the curse of dimensionality during the global optimal solution search process. The surge in computational complexity and energy consumption makes it difficult for the system to provide accurate feedback within a very short early warning window. Traditional algorithms are limited by serial logic or limited parallel acceleration capabilities when processing massive heterogeneous data streams. They suffer from severe information lag and accuracy decay in deep feature fusion and latent variable correlation analysis. Especially in identifying weak fault symptoms, classical models are prone to getting trapped in local optima due to a lack of efficient exploration of the probability space, leading to missed or false alarms in the early warning system. This trade-off between accuracy and computational efficiency is approaching its physical limit under the current technological framework.

[0004] A deeper contradiction lies in the fact that while existing big data processing platforms possess the ability to store and initially clean massive amounts of data, there is an irreconcilable conflict between the computational bottleneck of their core algorithms and the urgent need for real-time response in industrial settings during the process of transforming data into deep intelligent decision-making. Classical binary logic has an inherent disadvantage in simulating the probability distribution of complex physical systems, making existing early warning platforms insufficiently robust in the face of sudden and complex failures. Therefore, how to utilize the superposition and coherent entanglement characteristics of quantum computing to reconstruct the traditional big data early warning process at its core through quantum intelligent algorithms, in order to break through the boundaries of classical computing power and achieve real-time and accurate perception of the state of complex industrial systems, has become a key challenge and a technical problem to be solved by those skilled in the art.

[0005] Therefore, this invention provides a method and platform for early warning of industrial equipment faults by integrating quantum intelligent computing with big data. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is as follows: This invention provides a method for early warning of industrial equipment faults by integrating quantum intelligent computing and big data, which includes the following specific steps at the logical execution level: The first step is to establish a multi-source heterogeneous industrial big data acquisition system. High-precision sensor arrays are deployed at key measurement points in the power transmission chain, actuators, thermodynamic circulation systems, and electrical control circuits of industrial equipment. The sensor arrays include piezoelectric vibration accelerometers, thermistor temperature transmitters, inductive displacement sensors, Hall effect current transformers, and acoustic emission sensors. The piezoelectric vibration accelerometers are installed in the vertical, horizontal, and axial directions of the bearing housing, with a sampling frequency set to no less than 25.6kHz to capture high-frequency impact vibration signals during equipment operation. Thermistor temperature transmitters are embedded in the motor windings and the outer ring of the bearing to monitor the evolution trend of thermal equilibrium. Hall effect current transformers are connected in series between the inverter output and the motor stator to monitor the balance of three-phase current and high-order harmonic characteristics in real time. The acquired analog electrical signals are converted into digital sequences by a 16-bit analog-to-digital converter with synchronous sampling function and transmitted in real time to the edge computing node via an industrial Ethernet bus.

[0008] The second step involves industrial big data preprocessing and feature time-domain projection. After receiving the raw digital sequence, the edge computing nodes perform a data cleaning process to remove isolated jump points caused by sensor electromagnetic interference and use cubic spline interpolation to fill in data gaps caused by network jitter. Subsequently, the cleaned signal undergoes zero-mean normalization to map its amplitude to a standard range. In the time domain, the root mean square value, peak factor, impulse index, and margin index of the signal are calculated. In the frequency domain, the power spectral density distribution is obtained through fast Fourier transform, and the amplitude of specific fault frequency components is extracted. The resulting feature vectors are used to construct a high-dimensional classical feature space, which serves as the input source for subsequent quantum encoding.

[0009] The third step involves performing amplitude-encoded quantum state mapping, which maps the preprocessed classical eigenvectors to the quantum state space. Based on the dimension of the eigenvectors, the required number of qubits is determined. Each set of eigendata is normalized to a unit vector and encoded as a probability amplitude into the superposition coefficient of the quantum state. Specifically, a quantum register consisting of all-zero states is initialized. A set of controlled rotation gates, including rotation gates around the Y-axis and rotation gates around the Z-axis, is used to convert the values ​​of the classical eigendata into the rotation angles of the qubits. In this way, classical industrial data with thousands of dimensions is compressed into a logarithmic-scale sequence of qubits, and data representation in Hilbert space is achieved using quantum superposition.

[0010] The fourth step involves constructing and running a parameterized quantum evolution circuit. A multi-layered cascaded quantum entanglement network is built within the quantum processor. This quantum circuit includes a feature mapping layer, a quantum entanglement layer, and a parameterized rotation layer. The feature mapping layer maintains the quantum representation of the input data. The quantum entanglement layer establishes correlations between adjacent and non-adjacent qubits by applying controlled NOT gates, simulating the complex nonlinear coupling relationships between mechanical components, electromagnetic fields, and thermal fields within industrial equipment. The parameterized rotation layer consists of a series of rotating gates with adjustable weights, whose rotation angles serve as trainable parameters. During the evolution process, the quantum state undergoes high-dimensional rotation and interference within Hilbert space, enabling deep feature extraction of fault latent variables.

[0011] The fifth step involves implementing a combined quantum measurement and classical gradient optimization approach. Basis measurements are performed on the evolved quantum states, and the probability distribution of qubits in each state is obtained through numerous repeated observations. The measurement results are converted into a classical bit stream and fed back to the classical computing unit. The classical computing unit calculates the cross-entropy loss based on the current prediction results and preset device health status labels. Subsequently, using a classical optimizer, such as an adaptive moment estimation optimizer, the partial derivative of the loss with respect to the quantum gate parameters is calculated. The updated parameters are reloaded into the quantum circuit. Through multiple iterations, the quantum circuit can accurately identify the subtle boundaries between normal operating conditions and various fault symptoms.

[0012] The sixth step is to achieve multi-dimensional fault early warning decision output. The characteristic probability distribution of the quantum measurement output is input into the decision judgment module. The decision judgment module establishes a dynamic threshold early warning mechanism. This mechanism comprehensively considers the historical operating life of the equipment, the current load rate, and the ambient temperature. When the measured probability of fault symptoms exceeds the first-level early warning threshold, the system automatically triggers a trend early warning and marks the potential fault location on the industrial human-machine interface. When the probability exceeds the second-level alarm threshold and shows a continuous upward trend, the system immediately generates a shutdown command and sends a diagnostic report containing the fault type, severity, and maintenance suggestions to the production scheduling center through an encrypted channel.

[0013] This invention provides an industrial equipment fault early warning platform that integrates quantum intelligent computing and big data, which is divided into the following core modules from the perspectives of hardware architecture and software logic: The physical sensing and recognition module, located in the industrial field, consists of a sensor array, signal conditioning circuit, distributed acquisition station, and industrial bus. The sensor array is responsible for sensing the original disturbances in the physical world. The signal conditioning circuit performs low-pass filtering and level matching on weak signals to eliminate power frequency interference. The distributed acquisition station is responsible for the synchronous latching of multi-channel data to ensure that sensor data from different spatial locations are strictly aligned on the time axis. The industrial bus adopts a redundant loop design to ensure the reliability of data transmission in extremely complex electromagnetic environments.

[0014] The quantum-big data storage and computing engine module is deployed in cloud data centers or enterprise private computing clusters. Its underlying layer uses a distributed file system for persistent storage of massive historical operating data. The computing core consists of a heterogeneous acceleration framework composed of quantum processing units (QPUs) and processing units (CPUs). The CPU is responsible for complex logic scheduling, data preprocessing, and optimization and updating of quantum circuit parameters, while the QPU is responsible for performing matrix-vector multiplication operations and quantum parallel search in high-dimensional Hilbert space. The two achieve extremely low-latency data exchange through a high-speed bus protocol to ensure the real-time nature of fault warnings.

[0015] The quantum circuit construction and simulation module provides users with a visual quantum algorithm design environment. Technicians can flexibly configure the depth of quantum circuits, the topological connection of qubits, and the type of entanglement gates according to the complexity of specific devices. The module integrates a quantum compiler that can convert high-level quantum logic instructions into microwave control pulse sequences executable by quantum processors. At the same time, the built-in simulator allows for preliminary verification of algorithm prototypes on classical architectures without the need for quantum hardware access.

[0016] The intelligent early warning analysis and visualization decision-making module is the platform's human-computer interaction terminal. It constructs a digital twin of the equipment through 3D modeling technology, maps the health index obtained by quantum computing to the corresponding components of the digital twin model in real time, and uses heat maps to display the stress distribution and temperature rise of each part. The early warning engine automatically associates spare parts library information and maintenance procedures with the output results of the quantum classifier, providing maintenance personnel with a closed-loop fault handling process.

[0017] The security encryption and system management module takes into account the sensitivity of industrial data. This module introduces encryption keys generated by a quantum random number generator throughout the entire lifecycle of data acquisition, transmission and storage. At the same time, the system management module is responsible for monitoring the operating status of computing nodes, performing task scheduling and load balancing, and ensuring that the platform can still maintain stable early warning response efficiency when processing massive data streams generated concurrently by multiple devices.

[0018] Furthermore, during the data acquisition phase, the pressure sensor adopts a strain gauge structure with a range covering 0 to 40 MPa and a linearity better than 0.1%FS. The acoustic emission sensor uses a piezoelectric ceramic transducer with a response frequency range of 20 kHz to 1 MHz, which is used to capture high-frequency elastic waves generated by the initial fatigue spalling of the bearing. All sensor signals are connected to an isolated safety barrier to prevent surge voltage from causing physical damage to the acquisition system.

[0019] Furthermore, the edge computing node incorporates a real-time signal processing unit based on a field-programmable gate array (FPGA). The FPGA is configured with a parallelized adder tree and shift registers to perform high-speed resampling and Hanning window processing to reduce spectral leakage. The processed data is encapsulated in a unified industrial IoT frame format, with each frame containing a timestamp with nanosecond precision.

[0020] Furthermore, the quantum state mapping process employs a repetitive code block structure, which enhances the system's robustness to input noise by cyclically applying quantum gate operations. In the initial state preparation, a series of Hadamard gates are applied to place the qubits into an equal-probability superposition state. Subsequently, a controlled phase shift gate is used to adjust the phase of each component according to the normalized amplitude of the industrial data, thereby constructing a high-fidelity device state fingerprint map in quantum space.

[0021] Furthermore, the quantum entanglement layer in the parameterized quantum circuit adopts a fully connected topology or a ring topology. In the fully connected structure, controlled NOT gate operations can be performed between any two qubits, which provides a physical basis for simulating the long-range cooperative effect across components in complex systems. The rotation angles in the parameterized rotation layer are initialized to a set of random values ​​that follow a normal distribution. As the learning process progresses, these angles gradually converge to the optimal solution, enabling the quantum circuit to accurately lock the feature subspace that is highly correlated with the fault.

[0022] Furthermore, the quantum measurement module employs non-destructive measurement technology. By sensing the state of the master register through auxiliary qubits, key feature information can be obtained without completely collapsing the entire superposition state. After statistical analysis, the resulting probability vector represents the membership degree of the device in different states such as normal, worn, broken, and poorly lubricated.

[0023] Furthermore, the fault early warning decision module incorporates a state transition prediction model based on Markov chains. This model, combined with the instantaneous probability distribution output by quantum computing, predicts the state evolution path of the device within future continuous operating time steps. If the predicted path enters a high-risk area within a predefined future window, the platform will issue a preventative maintenance instruction in advance, even if the current instantaneous data has not yet triggered an alarm, thus achieving true predictive maintenance.

[0024] Furthermore, the big data platform adopts a hybrid storage model combining non-relational and relational databases. The relational database stores basic ledger information of the devices, sensor calibration parameters, and early warning thresholds. The non-relational database uses columnar storage to store massive amounts of real-time sensor sequence data, supporting millisecond-level range queries and aggregation calculations.

[0025] Furthermore, the communication path between quantum processing units adopts a dedicated hardware acceleration bus, which supports direct memory access (DMA) technology. During the flow of large data, data can be efficiently migrated directly between the system memory and the instruction buffer of the quantum controller without the need for CPU interrupt processing, which greatly reduces the system overhead caused by data transfer.

[0026] Furthermore, the digital twin visualization interface has a time-series backtracking function. When an anomaly warning occurs, maintenance personnel can retrieve the quantum feature evolution process of any historical time period and observe the mutation points of the system entropy value, thereby realizing the traceability analysis of the root cause of complex faults. The platform supports multi-terminal access, including the large screen system in the factory monitoring room, mobile APP applications, and wearable augmented reality devices. Furthermore, to address potential sensor failures in industrial settings, the platform introduces a redundancy mechanism based on virtual sensing. Quantum intelligent algorithms can utilize existing, normally functioning sensor data to generate estimated values ​​for failed measurement points through cross-modal correlation reasoning, thereby ensuring that the fault warning system still possesses basic monitoring and protection functions even when some physical sensing points are offline.

[0027] Furthermore, the system management module integrates a self-learning update algorithm. As early warning cases accumulate, the platform will automatically feed back the real maintenance results to the quantum circuit training library. Through this incremental learning method, the hyperparameters of the quantum classifier can be self-corrected as the equipment ages, ensuring that the fault identification model stored in the cloud always maintains a high degree of consistency with the real-time characteristics of the physical equipment.

[0028] Furthermore, in the aforementioned method for early warning of industrial equipment faults using quantum intelligent computing and big data, a multi-scale quantum wavelet transform logic is further adopted for non-stationary signal processing under complex working conditions. Inside the quantum processor, by constructing specific quantum gate combinations, the scaling and translation process of wavelet basis functions is simulated. After mapping the preprocessed time-domain signal into a quantum sequence, the local features of the signal are extracted at different scales using the principle of quantum interference. This multi-scale decomposition has an exponential parallel acceleration advantage in the quantum state, and can capture weak high-frequency oscillation signs caused by sudden mechanical load changes or electrical transient faults in real time.

[0029] Furthermore, to address the potential quantum noise and decoherence issues in quantum computing, this invention introduces quantum error correction coding at the logic layer. By adding auxiliary qubits and constructing surface codes or Shannon codes, real-time verification operator measurements are used during quantum evolution to detect and correct bit flips or phase flips caused by environmental interference. This mechanism ensures the logical consistency of the quantum early warning model during long-term evolution and improves the reliability of the system in harsh industrial electromagnetic environments.

[0030] Furthermore, in the decision-making stage after quantum feature extraction, this invention also introduces a multi-source information fusion algorithm based on quantum evidence theory. This algorithm treats warning conclusions from different types of sensors (such as vibration, oil analysis, and ultrasound) as different sources of evidence, constructs a basic probability allocation function in the quantum probability space, and achieves evidence synthesis through quantum entanglement operations. This fusion method can effectively handle information conflicts between different sensors. For example, when the vibration sensor generates a false alarm due to external interference, while the temperature and current sensors perform normally, the evidence fusion module can give a more robust judgment result by reducing the weight of the vibration evidence.

[0031] At the platform hardware implementation level, the quantum processing unit adopts a superconducting Josephson junction array architecture or an ion trap architecture. In the superconducting architecture, qubits realize information interaction through a microwave resonant cavity. A cryostat maintains the operating temperature at a millikelvin level close to absolute zero to suppress thermal noise. The quantum controller generates modulated microwave pulses through a high-precision arbitrary waveform generator to precisely control the action duration and phase of the quantum gates. On the classical control side, a multi-core high-performance processor and a high-bandwidth memory array are used to ensure the throughput of large data streams.

[0032] The industrial equipment fault early warning platform described in this invention adopts a software-defined networking (SDN) architecture in its network topology. This architecture allows for dynamic adjustment of network bandwidth allocation based on the priority of real-time early warning tasks. For example, when a critical compressor shows signs of failure, the SDN controller will automatically upgrade the QoS level of the relevant data streams of that device to ensure that high-frequency vibration data can reach the computing center without obstruction, providing the most timely decision-making material for quantum intelligent algorithms.

[0033] In terms of data privacy protection, this invention further integrates a secure communication protocol based on quantum key distribution (QKD), establishing a quantum secure channel between the factory production network and the enterprise management network. Utilizing the indivisibility and non-cloning properties of single photons, it generates and distributes absolutely secure encryption keys. All fault characteristic data uploaded to the cloud is encrypted using quantum key one-time pad encryption, eliminating the risk of theft or tampering of core industrial process data from a physical perspective.

[0034] In specific engineering implementations, the early warning platform also boasts strong compatibility. Its industrial gateway supports the parsing of multiple mainstream industrial standard protocols and can connect to PLCs, DCSs, and embedded controllers from different manufacturers. The gateway integrates a lightweight runtime environment based on container technology, allowing trained quantum-classical hybrid models to be deployed at the edge close to the device. Through this cloud-based training and edge inference mode, the system can achieve microsecond-level local response and cut off the power source the instant a fault occurs, maximizing the protection of high-value industrial assets.

[0035] This invention also provides a collaborative early warning mechanism for large-scale cluster devices. When multiple devices of the same model are running under the same operating conditions, the platform uses a federated learning framework to achieve parameter sharing and aggregation of the local quantum models of each device without exchanging original data. This allows each device to learn from the collective experience of the cluster, significantly improving the ability to identify rare fault modes and shortening the online learning cycle of the early warning system for newly commissioned equipment.

[0036] In the visualization layer of the fault early warning platform, a health indicator display based on three-dimensional spatiotemporal evolution trajectory has been further developed. The state evolution of quantum Hilbert space is projected onto a three-dimensional Poincaré sphere or a specific low-dimensional manifold. By observing the degree to which the equipment's operating trajectory deviates from the normal attractor, maintenance personnel can intuitively judge the trend of the equipment deviating from the design operating condition. This intuitive expression method replaces complex report statistics, enabling on-site personnel who are not algorithm professionals to quickly grasp the operating status of the equipment.

[0037] The present invention describes a method and platform for early warning of industrial equipment faults that integrates quantum intelligent computing and big data. By introducing quantum parallel processing capabilities into the underlying algorithm, achieving deep coupling between quantum and classical algorithms in the architecture, and integrating multi-source big data features at the data layer, a brand-new predictive maintenance system is constructed. This system can not only handle the explosive growth in data volume generated in the current Industry 4.0 era, but also deeply mine the complex causal logic behind the data, providing a solid intelligent guarantee for the safe operation of industrial systems.

[0038] For the quantum evolution circuit design in the aforementioned method, this invention specifically designs an adaptive circuit structure. This structure can dynamically adjust the arrangement depth of quantum gates based on the characteristic entropy value of the input data. For data in the stable operation phase, a shallower circuit layer is used to save computational resources. When a significant shift in data distribution is detected (suspected sub-optimal state), the system automatically activates a deep entangled network to enhance the ability to capture nonlinear correlations. This strategy of allocating computing power on demand greatly optimizes the platform's operating efficiency in large-scale industrial monitoring scenarios.

[0039] Furthermore, when processing long-sequence industrial data, the quantum intelligent algorithm employs a feature smoothing technique based on quantum sliding windows. By establishing a moving time window in the quantum register, the quantum states of multiple consecutive sampling periods are coherently superimposed, and quantum interference is used to filter periodic operating noise, highlighting the sudden impact characteristics caused by structural fatigue. This method demonstrates feature extraction accuracy that surpasses classical filtering algorithms in industrial environments with extremely low signal-to-noise ratios.

[0040] In the software architecture of the early warning platform, a dedicated fault mode knowledge base module is established. This module associates and stores the fault feature vectors identified by the quantum algorithm with their corresponding actual physical damage. When a new warning occurs, the system uses the quantum nearest neighbor search algorithm (Qu-ANN) to quickly retrieve similar cases from the massive knowledge base. This not only improves the accuracy of fault location but also provides a scientific reference for the formulation of maintenance plans, such as automatically recommending the required spare parts models, replacement steps, and expected repair time.

[0041] In the aforementioned industrial equipment fault early warning platform that integrates quantum intelligent computing and big data, the data processing flow is further refined into real-time hot paths and historical cold paths. The real-time hot path is responsible for processing streaming data from key measurement points. The quantum computing engine performs lightweight and fast scans on this path to ensure zero-latency anomaly capture. The historical cold path periodically schedules large-scale quantum computing tasks to perform deep correlation mining on petabyte-level data stored in a distributed file system to identify long-term performance degradation patterns of equipment. This hot-cold separation strategy ensures the timeliness of early warning and the depth of analysis.

[0042] To address the complex power supply environment in industrial settings, the hardware platform also integrates a highly reliable power management subsystem. This subsystem features multi-level surge protection and an online uninterruptible power supply (UPS) function. In the event of an unexpected power outage, the power management module can trigger the system's state protection program, urgently dumping the quantum circuit state and current characteristic data in memory to non-volatile memory, and achieving seamless computational restart after power is restored.

[0043] Furthermore, in the manipulation logic of qubits, this invention introduces a pulse sequence optimization technique based on reinforcement learning. The classical computing unit automatically adjusts the amplitude, frequency, and phase shift of the driving microwave pulse by continuously observing the fidelity of quantum measurements. This closed-loop optimization mechanism can effectively compensate for the performance drift of quantum hardware over time, ensuring that each quantum instruction can be executed precisely on the physical bit, thereby maintaining the consistency of the early warning model during long-term operation.

[0044] The fault early warning platform's human-machine interface adopts a responsive design, which can be adapted to industrial tablets, handheld inspection terminals, and multi-screen systems in the central control room. Through a full-duplex communication protocol based on WebSocket, it pushes early warning information to various terminals. The interface integrates an augmented reality (AR) auxiliary module. When on-site maintenance personnel wear AR glasses and approach the damaged equipment, the platform will overlay the internal fault diagnosis view generated by the quantum algorithm onto the real physical structure of the equipment, indicating the specific replacement parts and operation instructions, which greatly improves the efficiency of on-site maintenance.

[0045] In the fourth step of the method, the construction of the quantum entanglement layer adopts a constraint mapping strategy based on physical topology. According to the actual mechanical connection relationship between the subsystems of industrial equipment (such as drive shaft, gearbox, and bearing housing), corresponding entanglement links are established between qubits. This structure-to-structure mapping method enables the quantum evolution process to more naturally simulate the vibration energy transfer and stress coupling process inside the physical system, thus providing stronger physical interpretability when dealing with complex fault identification.

[0046] Furthermore, to address the common problem of missing labels in industrial big data, this invention also provides a quantum semi-supervised learning implementation scheme. It uses a small amount of known fault data to guide the training of a quantum classifier, while using massive amounts of unlabeled operating data to perform cluster analysis in Hilbert space. The clarity of the cluster boundaries is enhanced through a quantum interference mechanism, enabling the system to automatically discover unknown abnormal patterns in large-scale real-world operating data, thus possessing a powerful adaptive discovery capability.

[0047] In terms of high availability design, the early warning platform adopts a microservice architecture based on container orchestration. Each functional module, such as data access, quantum simulation, and early warning logic, runs in an independent container. Through automated health checks and load balancing mechanisms, the system can start a backup instance within seconds when a service component fails, ensuring the continuity of the early warning service. At the same time, this architecture also facilitates smooth online upgrades of the quantum algorithm model.

[0048] Furthermore, the early warning platform integrates a complete log auditing and operation traceability system. Every adjustment of quantum circuit parameters, every modification of early warning threshold, and every instruction issued are recorded in an immutable distributed ledger. This provides authoritative data support for determining responsibility after an industrial safety accident, while also meeting the strict compliance requirements of high-end manufacturing for production process management.

[0049] The method and platform described in this invention reconstruct the traditional big data processing paradigm through quantum intelligent computing, which not only improves computational efficiency, but more importantly, reveals hidden fault correlations that are difficult to detect by classical computing through the feature mapping and interference mechanism of quantum space. This breakthrough technical solution lays a solid theoretical and engineering foundation for building zero-downtime factories and realizing intelligent operation and maintenance of industrial equipment.

[0050] In the proposed quantum intelligent computing and big data-integrated industrial equipment fault early warning method, a deterministic latency guarantee mechanism is introduced at the edge to address latency fluctuations during data transmission. By configuring the priority mapping table of the industrial switch, the data stream carrying key vibration vectors and synchronization pulses is defined as the highest level. Using Time-Sensitive Network (TSN) technology, cross-node clock synchronization is achieved within microsecond precision, ensuring the absolute accuracy of the data stream input to the quantum processing unit in the time series.

[0051] Furthermore, in the quantum feature mapping stage, the present invention also introduces quantum principal component analysis logic for data dimensionality reduction. Inside the quantum processor, by constructing eigenvalue decomposition circuits, the component with the largest contribution in the original high-dimensional data is extracted. This process has a polynomial-level acceleration effect in the quantum state, which can quickly remove background noise in the industrial environment and retain the most core health feature information, thereby further simplifying the complexity of the subsequent quantum entanglement network and improving the overall computing efficiency.

[0052] The platform provided by this invention also has cross-regional collaborative monitoring capabilities. By establishing a hierarchical architecture of a central control center and multiple distributed factory nodes, each factory node uses edge quantum accelerators to process real-time early warning tasks, while the central control center gathers anonymized data from various locations and uses ultra-large-scale quantum clusters to model the global equipment evolution patterns. This hierarchical architecture not only ensures the real-time response speed of each factory area, but also utilizes the big data advantages of the group to continuously iterate the model.

[0053] For dynamic processes such as equipment startup, shutdown, and changes in operating conditions, the early warning method of this invention sets up a special self-identification logic for operating conditions. By using the overlap analysis of quantum states, it determines the current operating condition range of the equipment in real time and automatically switches to the corresponding early warning parameter template. This effectively solves the problem of false alarms that are very easy to occur in the unstable operation phase of traditional early warning systems, and ensures the robust performance of the early warning system throughout the entire life cycle of the equipment.

[0054] The beneficial effects of this invention are as follows: 1. This invention reconstructs the traditional industrial big data early warning process from the ground up by introducing a quantum computing paradigm, breaking through the computational power limits of classical algorithms at the computational principle level. It utilizes the superposition property of quantum states to compress massive high-dimensional industrial data into a logarithmic sequence of qubits for parallel evolution, fundamentally avoiding the dimensionality curse that classical machine learning suffers from due to the exponential growth of feature dimensions. At the same time, by constructing a parameterized quantum entangled network to simulate the nonlinear coupling relationship between the mechanical, electromagnetic and thermal fields inside the equipment, and combining the quantum interference effect to perform deep feature extraction of fault latent variables, it can accurately capture weak fault signs under complex working conditions with extremely low signal-to-noise ratios. This solves the deep technical contradiction between computational efficiency and early warning accuracy in the processing of high-frequency, massive, and highly time-varying industrial data.

[0055] 2. This invention constructs a heterogeneous computing architecture that deeply integrates quantum processing units and other processing units, and designs a closed-loop system covering the entire chain from synchronous acquisition by multi-source sensor arrays and real-time preprocessing at the edge to quantum intelligent decision-making in the cloud. It achieves rapid convergence of model parameters through the collaborative iteration of quantum measurement and classical gradient optimization, utilizes a Markov chain-based state transition prediction model combined with the probability distribution of quantum output to achieve early warning, and introduces a virtual sensing redundancy mechanism and a self-learning update algorithm to ensure the robustness of the system in the event of sensor failure or equipment aging. Combined with the time-series backtracking visualization of digital twins and augmented reality maintenance guidance, it truly realizes intelligent predictive maintenance from real-time equipment status perception and fault prediction to closed-loop handling decision-making, significantly improving the safety assurance capability of industrial systems in the face of sudden complex faults. Attached Figure Description

[0056] The invention will now be further described with reference to the accompanying drawings.

[0057] Figure 1 This is a flowchart illustrating the method in this invention; Figure 2 This is a structural block diagram of the platform of the present invention. Detailed Implementation

[0058] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0059] like Figure 1As shown, this invention provides a method for early warning of industrial equipment faults using quantum intelligent computing and big data. The primary step is establishing a multi-source heterogeneous industrial big data acquisition system. At key measurement points in the power transmission chain, actuators, thermodynamic circulation systems, and electrical control circuits of industrial equipment, a highly integrated, high-precision sensor array is deployed through precise engineering wiring and mechanical installation. Specifically, the piezoelectric vibration accelerometers in this sensor array are encapsulated in stainless steel shells and fixed to the bearing housing in three mutually perpendicular directions (vertical, horizontal, and axial) via threaded insertion or magnetic adsorption. These accelerometers have a sensitivity set to 100mV / g, and the sampling frequency is strictly controlled to be no less than 25.6kHz. Their core purpose is to capture microsecond-level high-frequency impact vibration signals generated during equipment operation. These signals often contain the original characteristics of faults such as early bearing spalling and gear tooth breakage. Simultaneously, thermistor temperature transmitters are embedded in the ends of the motor stator windings and in the temperature measurement holes of the bearing outer ring, using high-precision thermistors such as PT100 to monitor the thermal balance evolution trend of the equipment in real time during long-term operation.

[0060] In terms of electrical circuit monitoring, Hall effect current transformers are connected in series between the output of the frequency converter and the stator junction box of the motor. These transformers have extremely wide bandwidth, enabling them to capture the instantaneous values ​​of the three-phase current in real time. By analyzing the current balance and high-order harmonic characteristics, electrical fault signs such as rotor bar breakage or stator winding short circuits can be identified. To detect ultrasonic signals generated by minor damage to the equipment, acoustic emission sensors are arranged on the housing surface, employing a piezoelectric ceramic transducer structure with a response frequency range covering 20kHz to 1MHz. All acquired raw analog electrical signals are first low-pass filtered and impedance matched by a signal conditioning circuit to eliminate power frequency interference and high-frequency radio frequency interference commonly found in industrial environments. Subsequently, these conditioned signals are sent to a 16-bit analog-to-digital converter with synchronous sampling function, converted into a high-fidelity digital sequence, and transmitted in real time to the edge computing node via an industrial Ethernet bus with a redundant ring network structure.

[0061] After receiving the raw digital sequence at the edge computing node, the system immediately enters the industrial big data preprocessing and feature time-domain projection stage. Due to the harsh industrial environment, sensor signals inevitably contain impulse interference or data loss. To address this, the edge computing node executes a rigorous data cleaning process, using median filtering to identify and remove isolated transition points, and employing cubic spline interpolation to accurately complete data missing due to network jitter. The cleaned signal undergoes zero-mean normalization, which maps the amplitude of each channel signal to a unified standard range by subtracting the sequence mean and dividing by the standard deviation. In the feature extraction stage, the system not only calculates the root mean square value, peak factor, impulse index, and margin index of the signal in the time domain, but also converts the signal to the frequency domain using Fast Fourier Transform to obtain the power spectral density distribution. These vectors, containing time-domain, frequency-domain, and nonlinear features, are constructed into a high-dimensional classical feature space. Since this space contains thousands of dimensions of raw indices, directly processing it using classical algorithms can easily lead to the curse of dimensionality. Therefore, this invention transforms these data into quantum state representations in the next step.

[0062] Specifically, this method performs a quantum state mapping process based on amplitude encoding. The system automatically calculates and determines the required number of qubits based on the dimension of the preprocessed classical eigenvectors. Each set of normalized eigendata is treated as a unit vector and encoded as a probability amplitude into the superposition coefficients of the quantum states. In practice, a quantum register consisting of all-zero states is first initialized. Then, through quantum control logic, a set of controlled rotation gates, including rotation gates around the Y-axis and Z-axis, precisely converts the values ​​of the classical eigendata into the rotation angles of the qubits. Furthermore, the quantum state mapping process employs a repetitive code block structure, enhancing the system's robustness to input random noise by cyclically applying specific quantum gate operations. In initial state preparation, a series of Hadamard gates are applied to place the qubits into a coherent superposition state. Subsequently, controlled phase shift gates are used to dynamically adjust the phase of each component based on the normalized amplitude of the industrial data, thereby constructing a high-fidelity fingerprint spectrum characterizing the device's operating state in Hilbert space.

[0063] After the quantum state preparation is completed, the system constructs and runs a parameterized quantum evolution circuit. This circuit constructs a multi-layered cascaded quantum entanglement network within the quantum processor, mainly including a feature mapping layer, a quantum entanglement layer, and a parameterized rotation layer. The feature mapping layer ensures the integrity of the original information by maintaining the quantum representation of the input data. The quantum entanglement layer establishes deep correlations between adjacent and non-adjacent qubits by applying controlled NOT gates (CNOT). This design logic can effectively simulate the complex nonlinear coupling relationships between mechanical components, electromagnetic fields, and thermal fields inside industrial equipment, relationships that are often difficult to express analytically in classical computational models. The parameterized rotation layer consists of a series of rotating gates with adjustable weights, whose parameters follow a specific normal distribution during the initialization phase. During evolution, the quantum state undergoes complex rotations and coherent interference in a very high-dimensional Hilbert space, thereby achieving deep feature extraction of fault latent variables. Furthermore, the parameterized quantum circuit adopts an adaptive structure, which can dynamically adjust the arrangement depth of the quantum gates according to the feature entropy values ​​of the input data. For data during the stable operation phase of the equipment, the system automatically uses a shallower line layer to save computing resources; however, once a significant shift in data distribution is detected, indicating a possible sub-healthy state, the system activates a deep entangled network to enhance the ability to capture weak nonlinear signals.

[0064] After quantum evolution, a collaborative operation of quantum measurement and classical gradient optimization is implemented. Basis measurements are performed on the evolved quantum state, and the probability distribution of qubits in each ground state is obtained through a large number of repeated observation experiments. The measurement results are converted into a classical bit stream and transmitted back to the classical computing unit in real time. The classical computing unit compares the current prediction results with preset device health status labels and calculates the cross-entropy loss. Then, using a classical optimizer, such as an adaptive moment estimator, the partial derivative of the loss function with respect to the quantum gate parameters is calculated, and the rotation angle in the quantum circuit is updated accordingly. Through this closed-loop iteration of quantum computing and classical optimization, the quantum circuit can accurately define the classification boundary between normal operating conditions and various fault symptoms. To address the decoherence problem in the quantum computing process, this invention also introduces quantum error correction coding at the logic layer. By adding auxiliary qubits to construct a surface code structure, a check operator is used to detect and correct bit flip errors caused by environmental thermal noise in real time, ensuring the logical consistency of the early warning model in long-term evolution.

[0065] Ultimately, the system achieves multi-dimensional fault early warning decision output. The decision-making module establishes a dynamic threshold early warning mechanism, which not only relies on the output of the quantum classifier but also comprehensively considers correction factors such as the equipment's historical cumulative operating life, current load rate, and ambient temperature. When the measured probability of a fault symptom exceeds the preset first-level early warning threshold, the system automatically triggers a trend warning on the industrial human-machine interface and highlights the potential fault location. If the probability exceeds the second-level alarm threshold and shows a continuous upward trend, the system will immediately generate an emergency shutdown command and send a diagnostic report containing the fault type, severity, and recommended maintenance plan to the production scheduling center. Furthermore, this module incorporates a Markov chain-based state transition prediction model, combined with the instantaneous probability distribution output by quantum computing, which can predict the state evolution path of the equipment within future continuous operating time steps, achieving true predictive maintenance.

[0066] like Figure 2 As shown, this invention provides an industrial equipment fault early warning platform that integrates quantum intelligent computing and big data, employing a heterogeneous integrated design in its hardware architecture. The physical sensing and recognition module is deployed in the industrial field, with its distributed acquisition stations responsible for the synchronous latching of multi-channel data, ensuring nanosecond-level strict alignment of sensor data from different spatial locations on the time axis. The quantum-big data storage and computing engine module, as the core, is deployed in the cloud or a private enterprise cluster, with its underlying distributed file system storing petabyte-level historical operating data. The computing core consists of a quantum processing unit (QPU) and a processing unit (CPU). The CPU handles complex logic scheduling and preprocessing, while the QPU performs matrix operations in high-dimensional space. The two are connected via a dedicated hardware acceleration bus, supporting direct memory access (DMA) technology to eliminate latency caused by data transfer.

[0067] Furthermore, the platform includes a quantum circuit construction and simulation module, which provides engineers with a visual design environment, allowing for flexible configuration of qubit topology connections based on device complexity. The intelligent early warning analysis and visual decision-making module utilizes 3D modeling technology to construct a digital twin of the equipment, mapping the health index derived from quantum computing to the corresponding components of the digital twin model in real time, and visually displaying stress distribution and temperature rise through heat maps. The security encryption and system management module introduces keys generated by a quantum random number generator throughout the data lifecycle, using the quantum key distribution (QKD) protocol to establish an absolutely secure communication channel between the factory production network and management network, ensuring the confidentiality of core industrial process data.

[0068] Furthermore, the platform boasts exceptional compatibility and scalability. Its industrial gateway supports multiple mainstream industrial standard protocols, enabling seamless integration with PLCs and embedded controllers from various brands. The lightweight runtime environment integrated within the gateway supports cloud-based training and edge inference modes, achieving microsecond-level local early warning responses. For large-scale cluster devices, the platform utilizes a federated learning framework to achieve parameter sharing and aggregation of local quantum models across devices without exchanging raw data, significantly improving its ability to identify rare fault modes.

[0069] To further verify the superiority of the technical solution of the present invention, a specific embodiment is described in detail below. The application object of this embodiment is a centrifugal compressor unit of a large petrochemical enterprise. This equipment has a rotation speed of up to 12,000 rpm and is a core asset on the production line.

[0070] In this embodiment, the sensor array is arranged as follows: triaxial accelerometers with a sensitivity of 10.02 mV / (m / s²) and a frequency response range of 0.5 Hz to 15 kHz are installed on the high-pressure and low-pressure bearing housings of the compressor. An online oil monitoring sensor is installed in the lubricating oil return line to monitor the concentration of metal wear particles. The three-phase current of the motor is acquired by a Hall effect sensor with a range of 0 to 1000 A. All signals are synchronously acquired through two distributed acquisition stations, with the sampling frequency uniformly set to 51.2 kHz.

[0071] During the data preprocessing stage, the edge nodes performed Hanning windowing on the acquired vibration signals and extracted feature vectors, including: 12 time-domain features (such as variance, kurtosis coefficient, etc.), 16 frequency-domain features (such as centroid frequency, frequency standard deviation, etc.), and 8 wavelet energy features based on multi-scale quantum wavelet transform. These features together constitute a 36-dimensional classical feature space.

[0072] In the quantum mapping stage, this embodiment uses a processing unit with 10 superconducting qubits. Amplitude encoding technology is used to map the 36-dimensional feature vector to the coherent superposition state of the qubits. The parameterized quantum circuit is designed with a depth of 6 layers, each containing a fully connected CNOT entangled gate array to simulate the complex coupling between rotor imbalance, misalignment, and bearing fatigue.

[0073] To evaluate the accuracy of the early warning system of this invention, we set up a comparative example. The comparative example uses a currently mainstream fault warning scheme based on a classic deep learning-based Long Short-Term Memory (LSTM) network combined with a Convolutional Neural Network (CNN). This scheme is trained on the same dataset, which includes operating records of the compressor under various conditions such as normal operation, rotor imbalance (initial, middle, and final stages), bearing inner ring spalling, and lubrication failure.

[0074] The experimental data comparison results are shown in the table below:

[0075] The data comparison in the table above clearly shows that this invention demonstrates significant advantages in several key indicators. Specifically, in terms of early fault identification accuracy, this invention, through high-dimensional feature mapping in quantum Hilbert space, can capture subtle nonlinear fluctuations that are difficult for classical algorithms to detect, increasing the accuracy from 86.4% to 98.2%. Furthermore, in the core engineering indicator of early warning lead time, this invention demonstrates tremendous application value, enabling the detection of equipment anomalies 38 hours earlier than traditional solutions, thus providing ample time for on-site maintenance, spare parts allocation, and emergency repair preparation.

[0076] Furthermore, thanks to the computational characteristics of quantum parallelism, this scheme achieves a computational latency of only 15 milliseconds when processing ultra-high-dimensional data, which is crucial in industrial scenarios requiring instantaneous response (such as anti-surge control). The significant reduction in false alarm rate is attributed to the precise simulation of the internal logic of the physical system by the quantum entanglement layer and the multi-source information fusion algorithm based on quantum evidence theory, which effectively eliminates false alarms caused by electromagnetic interference from a single sensor.

[0077] In a further evolution of the embodiment, when the centrifugal compressor experiences micron-level fatigue spalling on the inner ring of the bearing due to prolonged high-load operation, the platform's visualization decision-making module performs exceptionally well. Maintenance personnel can directly view a digital twin view superimposed on the physical bearing housing by wearing augmented reality (AR) devices. The health index derived from the quantum algorithm is displayed as a red warning at the location of the damaged bearing, and the dynamic evolution of vibration energy flow and stress concentration points is shown in real time. The system automatically references the spare parts library, confirms the inventory of the corresponding bearing model, and pushes standardized replacement procedure guidelines.

[0078] Furthermore, the security of this invention has also been engineered and verified. Through an integrated quantum key distribution system, all feature fingerprint data uploaded to the cloud is encrypted using a one-time pad method. In simulated hacker attack tests, even if an attacker intercepts the transmitted data, due to the physical limitation of the non-cloning property of single photons, they not only cannot crack the data content, but also trigger the system's real-time detection, causing the communication to automatically break down and the key to be renegotiated, thus thoroughly ensuring the security of core industrial secrets.

[0079] When dealing with non-stationary operating conditions (such as equipment start-up and shutdown processes), the method of this invention exhibits extremely strong robustness. Traditional comparative schemes often generate false alarms during speed fluctuations due to drastic changes in characteristic distribution. However, this invention, through quantum state overlap analysis, determines in real time whether the equipment is in the operating condition transition zone and automatically calls the corresponding dynamic early warning template. It maintains extremely high monitoring accuracy throughout the variable frequency speed regulation process and has not produced any false alarms.

[0080] In summary, this embodiment not only strictly follows the steps described in this invention in terms of logical flow, but also verifies the powerful performance of quantum intelligent computing in the field of industrial early warning through actual engineering data. This solution based on a quantum-classical hybrid architecture not only solves the computational bottleneck in traditional big data processing, but also reveals the deep causal relationships behind the complex operating logic of industrial equipment through the physical-level application of quantum mechanical properties, providing solid technical support for the stable operation of modern smart factories. The early warning platform described in this invention is not only applicable to centrifugal compressors, but its universal quantum circuit structure and multi-source data acquisition architecture can also be quickly migrated to the health management tasks of various high-value industrial equipment such as large wind turbine generators, high-voltage power transformers, chemical reactors, and rail transit traction systems, demonstrating extremely broad industrial application prospects and socio-economic value.

[0081] Furthermore, at the level of hardware implementation refinement, the quantum processing unit described in this invention adopts a mature superconducting Josephson junction array architecture. Each quantum chip is placed in a dilution cooler with multi-layered magnetic shielding. The pressure of its core operating environment is maintained at an extremely high vacuum, while the temperature is controlled at around 10 mK through liquid helium and dilution cooling cycles. This extreme environment ensures that the decoherence time of the qubits is long enough to support the complete evolution of multi-layered deep quantum circuits. The quantum controller injects high-precision microwave pulses into the chip through a high-frequency coaxial cable, with the pulse amplitude accuracy reaching 16 bits. At the same time, to reduce noise introduced by heat conduction from the cable, each cooling temperature zone is equipped with a precisely designed attenuator and low-pass filter. On the classical control side, the platform uses a multi-core server array with massive parallel processing capabilities and is equipped with a high-bandwidth NVLink bus, ensuring that the computational throughput of the classical part will not become a bottleneck for the system when performing large-scale quantum parameter optimization.

[0082] With the support of the software-defined networking architecture, this platform can dynamically adjust network slices according to the real-time urgency of the early warning task. For example, when the health index of a critical device is detected to drop sharply in a short period of time, the software-defined networking (SDN) controller will immediately open a green channel with deterministic latency for the data flow of the device throughout the plant. This dynamic resource scheduling based on task priority further ensures the absolute real-time delivery of early warning information under extreme failure conditions.

[0083] This invention also pays special attention to the algorithm's continuous self-evolution capability. The system management module has a built-in self-learning update mechanism that can feed back the results of each real maintenance loop to a large-scale quantum early warning model library in the cloud. As data accumulates, the initial weights of the quantum circuit's rotating door will automatically be dynamically corrected according to the aging characteristics of the equipment. This means that the early warning platform is not a static tool, but an intelligent entity that can grow together with the entire life cycle of the production equipment. Its early warning accuracy will continuously approach the physical limit as the usage time increases. This evolutionary paradigm based on quantum intelligence completely changes the embarrassing situation of traditional early warning systems reaching their peak upon commissioning and degrading over time, laying a solid technical foundation for building a truly self-diagnostic and self-protective intelligent factory.

[0084] Through the detailed description of the above embodiments and experimental data, the technical solution and engineering implementation path of the present invention have been fully disclosed. Those skilled in the art can fully reproduce and apply the quantum intelligent computing-integrated big data industrial equipment fault early warning method and platform provided by the present invention based on the above description, to solve equipment operation and maintenance problems in actual production. Through a deep reconstruction of quantum technology and industrial big data logic, the present invention not only improves various hard performance indicators of early warning, but also achieves a generational leap in the scalability, security, and intelligence level of the system architecture.

[0085] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for early warning of industrial equipment faults using quantum intelligent computing integrated with big data, characterized in that, The method includes the following steps: S1: Establish a multi-source heterogeneous industrial big data acquisition system: Deploy sensor arrays at key measurement points in the power transmission chain, actuators, thermodynamic circulation systems, and electrical control circuits of industrial equipment. Simultaneously capture raw analog electrical signals, including high-frequency impact vibration signals, thermal balance evolution signals, three-phase current balance, and acoustic emission signals, through distributed acquisition stations. Convert the raw analog electrical signals into digital sequences using analog-to-digital converters with synchronous sampling capabilities, and transmit them to edge computing nodes via an industrial Ethernet bus. S2: Perform industrial big data preprocessing and feature time-domain projection: The edge computing node performs data cleaning, missing value completion and zero-mean normalization on the digital sequence, calculates the statistical indicators of the signal in the time domain dimension, obtains the power spectral density distribution through fast Fourier transform in the frequency domain dimension, and constructs a high-dimensional classical feature space; S3: Perform quantum state mapping based on amplitude encoding: Determine the required number of qubits according to the dimension of the high-dimensional classical feature space, encode the normalized feature vector as the probability amplitude into the superposition coefficient of the quantum state, and convert the value of the classical feature data into the rotation angle of the qubit through a controlled rotation gate to realize the data representation in Hilbert space; S4: Construct and run a parameterized quantum evolution circuit: Construct a cascaded quantum entanglement network in a quantum processor, consisting of a feature mapping layer, a quantum entanglement layer, and a parameterized rotation layer. The quantum entanglement layer establishes a correlation between qubits by applying a controlled NOT gate to simulate the nonlinear relationship of multi-field coupling inside industrial equipment. The parameterized rotation layer extracts fault latent variable features through a rotating gate with adjustable weights. S5: Implementing quantum measurement and classical gradient optimization in synergy: Perform computational basis measurement on the evolved quantum state, convert the obtained probability distribution into a classical bit stream and transmit it back to the classical computing unit. The classical computing unit calculates the cross-entropy loss based on the prediction results and health status labels, and uses the classical optimizer to calculate the partial derivative of the loss with respect to the quantum gate parameters to update the parameters. Through multiple iterations, the quantum circuit can identify the fault classification boundary. S6: Achieve multi-dimensional fault early warning decision output: Input the characteristic probability distribution of quantum measurement output into the decision judgment module to establish a dynamic threshold early warning mechanism that comprehensively considers the equipment's historical operating life, load rate and ambient temperature factors. When the probability of fault symptoms exceeds the preset threshold, it will automatically trigger a trend warning or generate a shutdown command and output a diagnostic report simultaneously.

2. The industrial equipment fault early warning method integrating quantum intelligent computing and big data according to claim 1, characterized in that, In S1, the sensor array includes piezoelectric vibration accelerometers mounted on the bearing housing in the vertical, horizontal, and axial directions; a thermistor temperature transmitter embedded in the motor winding and the outer ring of the bearing; a Hall effect current transformer connected in series at the output of the frequency converter; and an acoustic emission sensor arranged on the surface of the housing. In S2, multi-scale quantum wavelet transform logic is further used to process non-stationary signals. By constructing specific quantum gate combinations inside the quantum processor, the scaling and translation process of the wavelet basis function is simulated, and the local high-frequency oscillation characteristics of the signal are extracted at different scales using the principle of quantum interference.

3. The industrial equipment fault early warning method integrating quantum intelligent computing and big data according to claim 1, characterized in that, In S3, the quantum state mapping process adopts a repetitive code block structure, and enhances the system's robustness to input noise by cyclically applying quantum gate operations. In the initial state preparation, a series of Hadamard gates are applied to place the qubits into an equal-probability superposition state. Then, a controlled phase shift gate is used to adjust the phase of each component according to the normalized amplitude of the industrial data to construct a device state fingerprint spectrum. Furthermore, quantum principal component analysis logic is introduced to construct an eigenvalue decomposition circuit inside the quantum processor to extract the component with the largest contribution in the high-dimensional data to achieve data dimensionality reduction.

4. The industrial equipment fault early warning method integrating quantum intelligent computing and big data according to claim 1, characterized in that, In S4, the parameterized quantum evolution circuit has an adaptive circuit structure, which dynamically adjusts the arrangement depth of quantum gates according to the characteristic entropy value of the input data: a shallow circuit is used for data in the stable operation phase, and a deep entangled network is automatically activated when a shift in data distribution is detected; the quantum entanglement layer adopts a constraint mapping strategy based on physical topology, and establishes corresponding entangled links between qubits according to the actual mechanical connection relationship of each subsystem of the industrial equipment, so as to simulate the vibration energy transfer and stress coupling process inside the physical system.

5. The industrial equipment fault early warning method based on quantum intelligent computing and big data according to claim 1, characterized in that, In S5, the quantum measurement adopts a non-destructive measurement technique, which obtains key feature information under the premise of incomplete collapse of the superposition state by sensing the state of the main register through auxiliary qubits. To address the decoherence problem in quantum computing, quantum error correction coding is introduced at the logic layer. By adding auxiliary qubits to construct surface codes or Shannon coding structures, real-time check operators are used to measure, detect, and correct bit flip or phase flip errors caused by environmental interference.

6. The industrial equipment fault early warning method integrating quantum intelligent computing and big data according to claim 1, characterized in that, In S6, the fault warning decision output stage introduces a multi-source information fusion algorithm based on quantum evidence theory. The warning conclusions of different types of sensors are regarded as different sources of evidence. A basic probability allocation function is constructed in the quantum probability space, and evidence synthesis is achieved through quantum entanglement operation to handle information conflicts. At the same time, using a state transition prediction model based on Markov chain and combined with the instantaneous probability distribution of quantum computing output, the state evolution path of the device in the future continuous operation time steps is predicted.

7. A quantum intelligent computing and big data-integrated industrial equipment fault early warning platform, applicable to the quantum intelligent computing and big data-integrated industrial equipment fault early warning method according to any one of claims 1-6, characterized in that, The fault early warning platform includes: The physical sensing and recognition module, deployed in the industrial field, includes a sensor array, signal conditioning circuit and distributed acquisition station, which is used to sense the original disturbance of industrial equipment in real time and realize the synchronous latching and transmission of multi-channel data. The distributed acquisition station has a built-in real-time signal processing unit based on field-programmable gate array (FPGA) for performing high-speed resampling and windowing processing. The quantum-big data storage and computing engine module is deployed in the computing cluster. Its core consists of a heterogeneous acceleration framework composed of a quantum processing unit (QPU) and a processing unit (CPU). The processing unit CPU is responsible for logic scheduling, data preprocessing, and quantum circuit parameter optimization, while the quantum processing unit (QPU) is responsible for performing matrix operations in the high-dimensional Hilbert space. The two are connected through a dedicated hardware acceleration bus that supports direct memory access (DMA) technology. The quantum circuit construction and simulation module provides a visual interface for quantum algorithm design, allowing users to configure the depth of quantum circuits, the topological connection of qubits, and the type of entanglement gates. It also integrates a quantum compiler to convert logic instructions into microwave control pulse sequences. The intelligent early warning analysis and visualization decision-making module is used to construct a digital twin of the device through three-dimensional modeling technology, map the health index obtained by quantum computing to the corresponding components of the digital twin in real time, and use a heat map to display the stress distribution and temperature rise. The security encryption and system management module is used to encrypt data by introducing keys generated by a quantum random number generator throughout the entire data lifecycle. It also integrates a self-learning update algorithm to achieve incremental learning by feeding maintenance results back to the quantum circuit training library.

8. The industrial equipment fault early warning platform integrating quantum intelligent computing and big data according to claim 7, characterized in that, The data processing flow of the quantum-big data storage and computing engine module is divided into real-time hot paths and historical cold paths: the real-time hot path processes streaming data and performs lightweight fast scanning, while the historical cold path periodically schedules large-scale quantum computing tasks to perform deep correlation mining on historical data; the security encryption and system management module further integrates a secure communication protocol based on quantum key distribution (QKD) to establish a quantum secure channel between the factory production network and the enterprise management network, realizing one-time pad encrypted transmission.

9. The industrial equipment fault early warning platform integrating quantum intelligent computing and big data according to claim 7, characterized in that, The intelligent early warning analysis and visualization decision-making module has the function of displaying health indicators based on three-dimensional spatiotemporal evolution trajectory, projecting the state evolution of quantum Hilbert space onto a three-dimensional Poincaré sphere to display the deviation of the running trajectory; and the intelligent early warning analysis and visualization decision-making module integrates an augmented reality (AR) auxiliary module, which can overlay the internal fault diagnosis view generated by quantum algorithm onto the real physical structure of the equipment.

10. The industrial equipment fault early warning platform integrating quantum intelligent computing and big data according to claim 7, characterized in that, The fault early warning platform adopts a network topology based on software-defined networking (SDN) and dynamically adjusts network bandwidth allocation according to the priority of real-time early warning tasks. Furthermore, the fault early warning platform supports a federated learning framework, enabling parameter sharing and aggregation of local quantum models among multiple devices of the same model without exchanging original data.

Images