Method and device for realizing anomaly detection and early warning of industrial equipment

Abstract

The invention relates to the technical field of Internet of Things, and particularly provides an industrial equipment anomaly detection and early warning implementation method and device, and the method comprises the following steps: S1, carrying out the real-time data collection; s2, performing multi-dimensional data fusion processing; s3, transmitting the data to a data processing center; s4, data analysis; s5, performing anomaly detection and notification; and S6, alarming and feeding back. Compared with the prior art, the risk of production interruption can be reduced, the operation safety of equipment can be improved, and the maintenance cost and economic loss caused by equipment failure are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and specifically provides a method and a device for realizing abnormality detection and early warning of industrial equipment. Background Art

[0002] With the advent of the Industrial 4.0 era, the level of industrial automation and intelligence has been significantly improved. The modern industrial production environment is complex, with a wide variety of equipment, and extremely high requirements for the operating stability and safety of the equipment. Traditional industrial monitoring systems mostly rely on manual inspections or regular inspections, which are not only inefficient, but also difficult to achieve comprehensive real-time monitoring of the equipment status. When an abnormality occurs in the equipment, due to the lack of an immediate early warning mechanism, the fault often cannot be discovered and handled in time, which in turn causes more serious damage or even production accidents, causing huge economic losses and safety hazards to the company. Therefore, in order to improve production efficiency and ensure equipment safety, there is an urgent need for a system that can achieve real-time monitoring and has intelligent early warning functions to reduce failure rates, reduce downtime, and improve production safety.

[0003] Traditional IoT device alarm systems usually rely on single sensor data and lack effective data fusion mechanisms, which limits the accuracy of anomaly detection. The data transmission and processing of these systems are not real-time enough to adapt to changes in network conditions, and they usually do not have self-learning and optimization capabilities. In addition, traditional early warning systems often issue alarms based on fixed thresholds and cannot dynamically adjust early warning strategies according to actual working conditions, resulting in high false alarm and missed alarm rates. Summary of the invention

[0004] The present invention aims at solving the above-mentioned deficiencies of the prior art and provides a method for realizing abnormality detection and early warning of industrial equipment with strong practicability.

[0005] A further technical task of the present invention is to provide a device for realizing abnormality detection and early warning of industrial equipment that is reasonably designed, safe and applicable.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] A method for realizing abnormal detection and early warning of industrial equipment has the following steps:

[0008] S1, real-time data collection;

[0009] S2, multi-dimensional data fusion processing;

[0010] S3, data is transmitted to the data processing center;

[0011] S4, data analysis;

[0012] S5, anomaly detection and notification;

[0013] S6. Alarm and feedback.

[0014] Furthermore, in step S1, various types of sensors are used to realize real-time monitoring of the operating status of industrial equipment, support various real-time transmission technologies, real-time data transmission and preprocessing.

[0015] Furthermore, in step S2, the collected real-time data is clearly and standardizedly integrated according to preset requirements, time synchronization technology is used to ensure that data from different sensors are aligned in time, data integration technology is used to integrate data from different sensors, and middleware services are used to coordinate and integrate data streams from different sources. Multi-dimensional data analysis algorithms are applied to extract insights from the fused data.

[0016] Furthermore, in step S3, the transmission strategy is dynamically adjusted according to the network status and data importance, and the combination of edge computing and cloud computing is utilized to deploy computing nodes at the edge of the network to pre-process and quickly analyze the collected data. At the same time, the cloud center is responsible for more complex data analysis tasks.

[0017] Furthermore, in step S4, machine learning and deep learning algorithms are introduced to enable the model to continuously optimize itself, and online learning and incremental learning algorithms are used to achieve real-time updating and prediction of the model. As new data is continuously input, the analysis model is updated in real time;

[0018] After the model is updated, the new model state is used to make real-time predictions on upcoming or current data.

[0019] Furthermore, in step S5, a novel anomaly detection mechanism is used to perform anomaly analysis and detection according to the anomaly threshold setting of the data center, and an alarm notification is issued when the data reaches the defined preset threshold or alarm rule;

[0020] At the same time, machine learning algorithms are used to make real-time predictions, conduct trend analysis and issue early warnings on real-time data.

[0021] Furthermore, in step S6, relevant personnel are notified immediately when an abnormality is detected, and different levels of graded warnings are implemented according to the severity and type of the abnormality to ensure that response measures are taken in a timely manner;

[0022] Automatically adjust the equipment's operating parameters and initiate maintenance procedures based on the early warning results to prevent potential failures.

[0023] An implementation device for abnormality detection and early warning of industrial equipment includes: at least one memory and at least one processor;

[0024] The at least one memory is used to store a machine-readable program;

[0025] The at least one processor is used to call the machine-readable program to execute a method for realizing abnormality detection and early warning of industrial equipment.

[0026] Compared with the prior art, the method and device for realizing abnormality detection and early warning of industrial equipment of the present invention have the following outstanding beneficial effects:

[0027] (1) Improve timeliness and preventive measures: By monitoring the status of equipment in real time, the present invention can capture minor changes and potential anomalies in equipment operation in the first place, so that the maintenance team can take preventive measures to prevent small problems from turning into major failures. This rapid response capability significantly reduces unplanned downtime caused by sudden equipment failures and ensures production continuity.

[0028] (2) Optimize human resource allocation: With the help of intelligent analysis technology, the present invention reduces the need for regular inspection and diagnosis by professionals. The system can automatically analyze data and determine the health status of equipment, thereby reducing the workload of maintenance personnel and allowing them to focus on more complex and manual tasks.

[0029] (3) Improved warning accuracy: Using advanced data analysis algorithms, such as machine learning and model recognition, the present invention can accurately distinguish between normal fluctuations and true abnormal conditions, effectively reducing the false alarm rate. This ensures the accuracy of warning information, allowing maintenance teams to rely on the alarms provided by the system with greater confidence. The automatic warning mechanism can promptly notify relevant personnel at the early stage of anomalies, helping to avoid serious equipment damage and production accidents;

[0030] (4) Strengthening risk management: The setting of automatic early warning mechanism ensures that any detected anomaly will trigger timely notification, whether through SMS, email or other communication means. This greatly shortens the time interval between the discovery of anomalies and their processing, reducing the risk of damage caused by delayed response.

[0031] (5) Improved economic efficiency: Since equipment problems can be discovered and solved in a timely manner, the present invention helps to reduce production losses and maintenance costs caused by failures. At the same time, reducing unnecessary maintenance work also directly reduces operating costs and improves overall economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Attached Figure 1 It is a schematic diagram of a framework for realizing anomaly detection and early warning of industrial equipment;

[0034] Attached Figure 2 The present invention is a flowchart of a method for realizing abnormality detection and early warning of industrial equipment. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] A best embodiment is given below:

[0037] like Figure 1 , 2 As shown, a method for realizing abnormality detection and early warning of industrial equipment in this embodiment has the following steps:

[0038] S1, real-time data collection;

[0039] This method uses various types of sensors to achieve real-time monitoring of the operating status of industrial equipment. It collects equipment data such as temperature, humidity, pressure, vibration, current, etc. in real time. It supports a variety of real-time transmission technologies such as low-latency wireless transmission, high-speed wired transmission, and edge computing nodes deployed near the equipment to achieve real-time data transmission and preprocessing, ensuring that the sensor has high sensitivity and fast response time to accurately capture real-time data and reduce transmission delays.

[0040] S2, multi-dimensional data fusion processing;

[0041] Based on sensors of multiple source types, data of multiple dimensions is collected. The collected real-time data is clarified and standardized according to preset requirements to eliminate noise and inconsistency. Time synchronization technology is used to ensure that data from different sensors are aligned in time. Data integration technology, such as feature mapping or data cascading, is used to integrate data from different sensors.

[0042] Leverage middleware services such as message queues (MQTT, Kafka, etc.) to coordinate and integrate data streams from different sources. Apply multidimensional data analysis algorithms such as multivariate regression, principal component analysis (PCA), or machine learning models to extract insights from the fused data. Combine advanced data fusion techniques such as Kalman filters or Bayesian networks to improve the accuracy of data fusion.

[0043] S3, data is transmitted to the data processing center;

[0044] The invention uses adaptive data transmission function to dynamically adjust the transmission strategy according to network conditions and data importance, such as giving priority to transmitting key data when the network is congested.

[0045] By combining edge computing and cloud computing, computing nodes are deployed at the edge of the network to pre-process and quickly analyze the collected data, reducing data transmission volume and latency. At the same time, the cloud center is responsible for more complex data analysis tasks, improving the response speed and processing capabilities of the entire system.

[0046] S4, data analysis;

[0047] The introduction of machine learning and deep learning algorithms enables the model to continuously optimize itself and improve prediction accuracy. The use of online learning and incremental learning algorithms enables real-time updates and predictions of the model. As new data is continuously input, the system can update the analysis model in real time to ensure that the early warning system remains sensitive and accurate to new or changing operating conditions. After the model is updated, the new model state can be used to make real-time predictions of upcoming data or current data. This can be used for fault detection, trend analysis, result feedback, etc.

[0048] S5, anomaly detection and notification;

[0049] Adopt new anomaly detection mechanisms, such as methods based on statistical process control (SPC), or combine time series analysis and model recognition technology. At the same time, perform anomaly analysis and detection according to the abnormal threshold setting of the data center, and issue an alarm notification when the data reaches the defined preset threshold or alarm rule. At the same time, use machine learning algorithms to perform real-time prediction, trend analysis and pre-alarm on real-time data.

[0050] S6, warning and feedback;

[0051] Immediately notify relevant personnel when an abnormality is detected. Implement different levels of graded warnings based on the severity and type of the abnormality to ensure timely response measures. At the same time, it can automatically adjust the operating parameters of the equipment and start maintenance procedures based on the warning results to prevent potential failures.

[0052] Based on the above method, a device for realizing abnormality detection and early warning of industrial equipment in this embodiment includes: at least one memory and at least one processor;

[0053] The at least one memory is used to store a machine-readable program;

[0054] The at least one processor is used to call the machine-readable program to execute a method for realizing abnormality detection and early warning of industrial equipment.

[0055] The above-mentioned specific implementations are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above-mentioned specific implementations. Any technical solutions that conform to the above-mentioned specific implementations of the present invention and any appropriate changes or substitutions made by ordinary technicians in the relevant technical field shall fall within the patent protection scope of the present invention.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for realizing abnormality detection and early warning of industrial equipment, characterized in that: The steps are as follows: S1, real-time data collection; S2, multi-dimensional data fusion processing; S3, data is transmitted to the data processing center; S4, data analysis; S5, anomaly detection and notification; S6. Alarm and feedback.

2. The method for realizing abnormality detection and early warning of industrial equipment according to claim 1, characterized in that: In step S1, various types of sensors are used to realize real-time monitoring of the operating status of industrial equipment, support various real-time transmission technologies, real-time data transmission and preprocessing.

3. The method for realizing abnormality detection and early warning of industrial equipment according to claim 2, characterized in that: In step S2, the collected real-time data is clearly and standardizedly integrated according to preset requirements. Time synchronization technology is used to ensure that data from different sensors are aligned in time. Data integration technology is used to integrate data from different sensors. Middleware services are used to coordinate and integrate data streams from different sources. Multi-dimensional data analysis algorithms are applied to extract insights from the fused data.

4. The method for realizing abnormality detection and early warning of industrial equipment according to claim 3, characterized in that: In step S3, the transmission strategy is dynamically adjusted according to the network status and data importance. By combining edge computing and cloud computing, computing nodes are deployed at the edge of the network to pre-process and quickly analyze the collected data. At the same time, the cloud center is responsible for more complex data analysis tasks.

5. The method for realizing abnormality detection and early warning of industrial equipment according to claim 4, characterized in that: In step S4, machine learning and deep learning algorithms are introduced to enable the model to continuously optimize itself. Online learning and incremental learning algorithms are used to achieve real-time updating and prediction of the model. As new data is continuously input, the analysis model is updated in real time. After the model is updated, the new model state is used to make real-time predictions on upcoming or current data.

6. The method for realizing abnormality detection and early warning of industrial equipment according to claim 5, characterized in that: In step S5, a new anomaly detection mechanism is used to perform anomaly analysis and detection according to the data center anomaly threshold setting. When the data reaches the defined preset threshold or alarm rule, an alarm notification is issued; At the same time, machine learning algorithms are used to make real-time predictions, conduct trend analysis and issue early warnings on real-time data.

7. The method for realizing abnormality detection and early warning of industrial equipment according to claim 6, characterized in that: In step S6, relevant personnel are notified immediately when an abnormality is detected, and different levels of graded warnings are implemented according to the severity and type of the abnormality to ensure that response measures are taken in a timely manner; Automatically adjust the equipment's operating parameters and initiate maintenance procedures based on the early warning results to prevent potential failures.

8. A device for realizing abnormal detection and early warning of industrial equipment, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to call the machine-readable program to execute the method according to any one of claims 1 to 7.

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