Abnormal early warning method based on equipment operation state

Abstract

The invention discloses an abnormity early warning method based on an equipment operation state, and the method comprises the following steps: 1, carrying out the demand analysis and planning, i.e., determining a monitoring target, selecting monitoring parameters, and determining an early warning level; 2, after a demand is determined, hardware installation and configuration are carried out; step 3, data acquisition and transmission, that is, data acquisition of hardware equipment is completed through the installed configuration; 4, performing data analysis and modeling on the acquired data; and 5, establishing an early warning mechanism, and carrying out early warning notification. According to the invention, the defects of a traditional equipment monitoring and maintenance mode can be effectively overcome, and the higher requirements of modern industrial production on equipment operation safety and efficiency are met.

Description

Technical Field

[0001] The present invention relates to the field of early warning methods, and in particular to an abnormal early warning method based on equipment operating status. Background Art

[0002] In modern industrial production, the technical complexity of equipment is constantly increasing, and the operating environment is changing, which leads to frequent equipment failures, seriously affecting production continuity and product quality. For example, in laboratories in the chemical and pharmaceutical industries, the efficient and stable operation of equipment such as centrifuges, ovens, and ventilation systems is crucial to the experimental results.

[0003] Traditional manual inspection and regular maintenance methods have problems such as untimely warning and low efficiency. Manual inspection relies on personnel's experience and working hours, and it is difficult to grasp the equipment status in real time; regular maintenance may lead to over-maintenance or under-maintenance, and cannot accurately respond to the actual operation of the equipment. Therefore, an abnormal warning method based on the equipment operation status is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problems of untimely warning and low efficiency in the existing abnormal warning method, and provide an abnormal warning method based on the operating status of the equipment.

[0005] The present invention solves the above technical problems through the following technical solutions, and the present invention comprises the following steps:

[0006] Step 1: Demand analysis and planning, namely, clarifying monitoring objectives, selecting monitoring parameters and determining warning levels;

[0007] Step 2: After determining the requirements, install and configure the hardware;

[0008] Step 3: Data collection and transmission, that is, completing the hardware equipment for data collection through the installed configuration;

[0009] Step 4: Analyze and model the collected data;

[0010] Step 5: Establish an early warning mechanism and issue early warning notifications.

[0011] Furthermore, the specific process of the demand analysis and planning is as follows:

[0012] Determine the types of laboratory equipment that need to be monitored (e.g., centrifuges, ovens, ventilation systems, etc.);

[0013] Clarify the functional requirements of the early warning system, including real-time monitoring, automatic alarm, and data recording;

[0014] Select monitoring parameters according to the operating characteristics of the equipment;

[0015] Determine the warning level. Warnings are divided into different levels, and corresponding handling measures and notification methods are set for each level.

[0016] Furthermore, the specific process of the hardware installation is as follows:

[0017] Sensor installation: Install sensors at the preset locations of the equipment to ensure that the installation location of the sensors can accurately reflect the operating status of the equipment;

[0018] Data acquisition device configuration, select the data acquisition device, and connect the sensor to the data acquisition device;

[0019] Configure the parameters of the data acquisition equipment, including sampling frequency, data format, etc.;

[0020] Network connection: connect the data acquisition equipment to the central monitoring system via a wired or wireless network, and perform network testing at the same time. If the test passes, the connection will be made.

[0021] Furthermore, the specific process of the network test is as follows: collecting network signal strength information, network transmission speed information and abnormal fluctuation information;

[0022] Analyze the network signal strength information. If the network signal strength information is less than the preset value for more than the preset time, it means the test fails. Otherwise, it means it passes.

[0023] Analyze the network transmission speed information. If the network transmission speed information is less than the preset value for a preset period of time, it means the test fails. Otherwise, it means it passes.

[0024] Abnormal fluctuation information means that the network fluctuation is greater than the preset value. The number of times the abnormal fluctuation information occurs within the preset time length is extracted. When the number of times the abnormal fluctuation information occurs is greater than the preset value, it means that the test fails, otherwise it means that the test passes.

[0025] Furthermore, the specific process of step three is as follows:

[0026] The data acquisition device collects sensor data in real time at a set frequency;

[0027] The collected data includes equipment operating parameters and equipment status information;

[0028] The collected data is transmitted to the central monitoring system through the network. During the data transmission process, encryption technology is used to encrypt the data;

[0029] The encrypted data is stored in the database.

[0030] Furthermore, the specific process of data analysis and modeling of the collected data is as follows:

[0031] Data preprocessing: clean the collected data to remove noise data and outliers;

[0032] Standardize the data to make it meet the requirements of the analysis model;

[0033] Establish a normal operation model and collect historical data of the equipment during normal operation as sample data;

[0034] Use machine learning algorithms to build a model of the normal operating status of the equipment. The model can describe the parameter change patterns of the equipment during normal operation.

[0035] Establish an anomaly detection model. According to the physical characteristics and operating rules of the equipment, establish an anomaly detection model. The model can identify abnormal changes in the operating status of the equipment.

[0036] Furthermore, the specific process of establishing the early warning mechanism and issuing early warning notifications is as follows:

[0037] Set warning thresholds based on the equipment's operating parameters and historical data;

[0038] The threshold is a dynamic threshold or a fixed threshold;

[0039] Real-time monitoring and analysis: receiving equipment operation data in real time and analyzing it through anomaly detection models;

[0040] When the equipment's operating status is abnormal, the system immediately triggers an early warning.

[0041] Compared with the prior art, the present invention has the following advantages: the abnormal warning method based on the equipment operation status can improve the equipment operation efficiency, real-time monitoring and rapid response, and can timely discover potential problems by collecting equipment operation status data in real time, avoiding prolonged downtime caused by equipment failure. The rapid response mechanism ensures that the equipment can be quickly processed when an abnormality occurs, reducing equipment idle time and improving equipment utilization;

[0042] Based on the analysis of equipment operation data, equipment maintenance plans can be arranged more accurately to avoid over-maintenance or under-maintenance, extend the service life of equipment, and reduce the full life cycle cost of equipment;

[0043] Enhance laboratory safety and prevent safety accidents caused by equipment failure: By monitoring key parameters such as vibration and temperature, abnormal operating conditions of equipment can be discovered in advance, avoiding safety accidents caused by equipment failure, such as fire and explosion, and ensuring the safety of laboratory personnel and equipment;

[0044] Reduce equipment maintenance costs: By discovering equipment anomalies early and handling them in a timely manner, we can avoid equipment failures from expanding and reduce the number and cost of equipment maintenance. At the same time, we can extend the service life of equipment, reduce the frequency of equipment updates, and further reduce operating costs.

[0045] The early warning system can automatically monitor the status of equipment and issue alarms, reducing the workload and time cost of manual inspections. Managers and maintenance personnel can devote more energy to core work and improve work efficiency.

[0046] Data encryption and secure transmission: Encryption technology is used during data transmission to ensure data security and integrity, prevent data from being tampered with or leaked, and ensure system reliability;

[0047] Network stability test: Perform network testing during hardware installation to ensure that the network connection between the data acquisition equipment and the central monitoring system is stable and reliable, avoid data loss or transmission delays due to network problems, and improve the overall stability of the system;

[0048] Machine learning and intelligent analysis: Using machine learning algorithms to establish normal operation models and anomaly detection models can automatically learn the operating rules of equipment, identify complex and hidden abnormal patterns, improve the accuracy and sensitivity of early warnings, and reduce false alarms and missed alarms.

[0049] Dynamically adjust the warning threshold according to the actual operation data of the equipment, so that the system can better adapt to the changes in the equipment operation status and further improve the intelligence level of the warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is the overall flow chart of the present invention. DETAILED DESCRIPTION

[0051] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0052] like Figure 1 As shown, this embodiment provides a technical solution: an abnormal warning method based on the operation status of the device, comprising the following steps:

[0053] Step 1: Demand analysis and planning, namely, clarifying monitoring objectives, selecting monitoring parameters and determining warning levels;

[0054] Step 2: After determining the requirements, install and configure the hardware;

[0055] Step 3: Data collection and transmission, that is, completing the hardware equipment for data collection through the installed configuration;

[0056] Step 4: Analyze and model the collected data;

[0057] Step 5: Establish an early warning mechanism and issue early warning notifications.

[0058] The specific process of demand analysis and planning is as follows:

[0059] Determine the types of laboratory equipment that need to be monitored (e.g., centrifuges, ovens, ventilation systems, etc.);

[0060] Clarify the functional requirements of the early warning system, including real-time monitoring, automatic alarm, and data recording;

[0061] Select monitoring parameters according to the operating characteristics of the equipment;

[0062] For example: temperature sensors are used to monitor the operating temperature of equipment;

[0063] Vibration sensors are used to monitor the mechanical vibration of equipment;

[0064] Current sensors are used to monitor the power usage of equipment;

[0065] Determine the warning level. Warnings are divided into different levels, and corresponding handling measures and notification methods are set for each level.

[0066] The specific process of the hardware installation is as follows:

[0067] Sensor installation: Install sensors at preset locations on the equipment to ensure that the sensor’s installation location can accurately reflect the equipment’s operating status; for example, install a temperature sensor on the centrifuge’s motor housing and a vibration sensor on the equipment’s mechanical parts.

[0068] Data acquisition equipment configuration, select data acquisition equipment (such as data acquisition card, PLC, etc.), and connect the sensor to the data acquisition equipment;

[0069] Configure the parameters of the data acquisition equipment, including sampling frequency, data format, etc.;

[0070] Network connection: connect the data acquisition equipment to the central monitoring system through a wired or wireless network, and perform network testing at the same time. Once the test passes, the connection will be made;

[0071] By clarifying the types of laboratory equipment that need to be monitored, it is possible to select monitoring parameters and sensors in a targeted manner according to the operating characteristics and importance of different equipment, ensuring that the monitoring system can accurately reflect the actual operating status of the equipment. For example, the temperature and vibration monitoring of the centrifuge, the temperature and humidity monitoring of the oven, the wind speed and pressure monitoring of the ventilation system, etc. These parameters are closely related to the normal operation of the equipment and can effectively capture abnormal conditions in the operation of the equipment.

[0072] It avoids blind installation of sensors and configuration of monitoring systems, reduces unnecessary investment, and improves the cost-effectiveness of the monitoring system.

[0073] According to the equipment operating parameters and historical data, the warnings are divided into different levels, and corresponding processing measures and notification methods are set for each level. This hierarchical warning mechanism can take different response measures according to the severity of the abnormality, avoiding neglect due to excessive alarms or missing the processing opportunity due to insufficient alarms.

[0074] By accurately monitoring the operating status of the equipment and issuing early warnings in a timely manner, safety accidents caused by equipment failures, such as fire, explosion, leakage, etc., can be effectively prevented. For example, when the temperature of the oven rises abnormally, the temperature sensor can capture this change in time, trigger an early warning, and avoid the risk of fire caused by excessive temperature. 3. Improve system stability and reliability Ensure that the sensor installation position is reasonable: Install the sensor at the preset position of the equipment to ensure that the installation position of the sensor can accurately reflect the operating status of the equipment. For example, install a temperature sensor on the motor housing of the centrifuge and install a vibration sensor on the mechanical parts of the equipment. These locations can directly monitor the key parameters of the equipment operation and improve the accuracy and reliability of the monitoring data.

[0075] By configuring the parameters of the data acquisition device (such as sampling frequency, data format) and performing network testing, ensure that the data acquisition device can stably transmit sensor data to the central monitoring system. Network testing includes collecting network signal strength information, network transmission speed information and abnormal fluctuation information, and judging whether the network connection is stable based on the test results. Only after the test is passed can the connection be made, which effectively avoids data loss or transmission delay caused by network problems and improves the overall stability of the system.

[0076] Providing high-quality raw data: By properly selecting monitoring parameters and sensors, ensuring that the sensor installation location is reasonable and data collection and transmission are stable, high-quality raw data can be provided for subsequent data analysis and modeling. These data are the basis for establishing the normal operation model of the equipment and the abnormality detection model. The quality of the data directly affects the accuracy and reliability of the model.

[0077] High-quality data can support the application of more complex machine learning algorithms or statistical analysis methods, thereby establishing a more accurate model of equipment operation status. For example, machine learning algorithms such as support vector machines, decision trees, and neural networks can be used to establish a normal operation model, and anomaly detection algorithms such as isolation forests and autoencoders can be used to establish anomaly detection models. These models can more accurately identify abnormal changes in equipment operation status and improve the intelligence level of the early warning system.

[0078] The specific process of the network test is as follows: collecting network signal strength information, network transmission speed information and abnormal fluctuation information;

[0079] Analyze the network signal strength information. If the network signal strength information is less than the preset value for more than the preset time, it means the test fails. Otherwise, it means it passes.

[0080] Analyze the network transmission speed information. If the network transmission speed information is less than the preset value for a preset period of time, it means the test fails. Otherwise, it means it passes.

[0081] Abnormal fluctuation information means that the network fluctuation is greater than the preset value. The number of times the abnormal fluctuation information occurs within the preset time length is extracted. When the number of times the abnormal fluctuation information occurs is greater than the preset value, it means that the test fails, otherwise it means that the test passes.

[0082] The specific process of step three is as follows:

[0083] The data acquisition device collects sensor data in real time at a set frequency;

[0084] The collected data include equipment operating parameters (such as temperature, pressure, vibration frequency, etc.) and equipment status information (such as operating time, downtime, etc.);

[0085] The collected data is transmitted to the central monitoring system through the network. During the data transmission process, encryption technology is used to encrypt the data;

[0086] The encrypted data is stored in the database.

[0087] The specific process of data analysis and modeling of the collected data is as follows:

[0088] Data preprocessing: clean the collected data to remove noise data and outliers;

[0089] Standardize the data to make it meet the requirements of the analysis model;

[0090] Establish a normal operation model and collect historical data of the equipment during normal operation as sample data;

[0091] Use machine learning algorithms (such as support vector machines, decision trees, neural networks) or statistical analysis methods (such as principal component analysis, correlation analysis) to establish a model of the normal operating status of the equipment. The model can describe the parameter change patterns of the equipment during normal operation.

[0092] Establish an anomaly detection model. According to the physical characteristics and operating rules of the equipment, establish an anomaly detection model that can identify abnormal changes in the operating status of the equipment. You can use threshold-based detection methods (such as alarms when temperature and pressure exceed set thresholds), or you can use machine learning-based anomaly detection algorithms (such as isolation forests and autoencoders).

[0093] The data acquisition equipment collects sensor data in real time at a set frequency, ensuring that managers can obtain the operating status information of the equipment in a timely manner. This real-time feature enables managers to respond quickly when equipment abnormalities occur, avoiding further deterioration of equipment failures.

[0094] The collected data includes not only equipment operating parameters (such as temperature, pressure, vibration frequency, etc.), but also equipment status information (such as operating time, downtime, etc.). This comprehensive data collection can more completely reflect the operation of the equipment and provide richer information for subsequent analysis and modeling.

[0095] Data encryption transmission: Encryption technology is used to encrypt data during data transmission to ensure data security and integrity. This effectively prevents data from being tampered with or leaked during transmission and ensures the reliability of the system.

[0096] The encrypted data is stored in the database for subsequent analysis and query. This stable data storage method ensures the long-term availability of data and provides data support for the long-term operation management and maintenance of the equipment.

[0097] The collected data is cleaned, noise data and outliers are removed, and standardized. This process can significantly improve the quality of the data, making it more in line with the requirements of the analysis model, thereby improving the accuracy and reliability of the model.

[0098] By collecting historical data of normal equipment operation as sample data, a model of normal equipment operation status is established using machine learning algorithms or statistical analysis methods. This model can accurately describe the parameter change pattern of the equipment during normal operation and provide a benchmark for subsequent anomaly detection.

[0099] According to the physical characteristics and operating rules of the equipment, an abnormality detection model is established. This model can identify abnormal changes in the operating status of the equipment. Whether it is a simple detection method based on thresholds or a complex detection algorithm based on machine learning, it can effectively discover potential problems in the operation of the equipment.

[0100] The specific process of establishing the early warning mechanism and issuing early warning notifications is as follows:

[0101] Set warning thresholds based on the equipment's operating parameters and historical data;

[0102] The threshold is a dynamic threshold or a fixed threshold;

[0103] Real-time monitoring and analysis: receiving equipment operation data in real time and analyzing it through anomaly detection models;

[0104] When the equipment operation status is abnormal, the system immediately triggers an early warning;

[0105] The warning thresholds are set based on the equipment's operating parameters and historical data. These thresholds can be dynamic and can be adjusted based on the equipment's actual operating conditions. This dynamic adjustment mechanism enables the system to more accurately identify abnormal situations and reduce false positives and false negatives.

[0106] The system receives equipment operation data in real time and analyzes it through anomaly detection models. When an abnormality occurs in the equipment operation status, the system can immediately trigger an early warning. This real-time monitoring and rapid response mechanism ensures that equipment anomalies can be discovered and handled in a timely manner.

[0107] Reduce safety risks: By timely detecting equipment anomalies and issuing early warnings, safety accidents caused by equipment failures can be effectively prevented. For example, when the temperature or pressure of the equipment exceeds the safe range, the system can immediately issue an alarm to remind personnel to take measures to avoid safety accidents such as fire and explosion caused by equipment failure.

[0108] The early warning mechanism can automatically identify equipment anomalies and notify relevant personnel, reducing the workload of manual inspections. Managers can devote more energy to core work and improve work efficiency.

[0109] Different warning methods are used according to the severity of the abnormality. This classification mechanism enables managers to reasonably allocate resources according to the warning level, give priority to severe abnormalities, reasonably arrange the inspection and handling of medium abnormalities, and record and continuously monitor minor abnormalities. This resource allocation method improves management efficiency and avoids untimely handling of equipment failures due to insufficient resources or unreasonable allocation.

[0110] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0111] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0112] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An abnormal warning method based on equipment operation status, characterized in that: The following steps are involved: Step 1: Demand analysis and planning, namely, clarifying monitoring objectives, selecting monitoring parameters and determining warning levels; Step 2: After determining the requirements, install and configure the hardware; Step 3: Data collection and transmission, that is, completing the hardware equipment for data collection through the installed configuration; Step 4: Analyze and model the collected data; Step 5: Establish an early warning mechanism and issue early warning notifications.

2. The abnormal warning method based on the equipment operation status according to claim 1 is characterized by: The specific process of demand analysis and planning is as follows: Determine the types of laboratory equipment that need to be monitored (e.g., centrifuges, ovens, ventilation systems, etc.); Clarify the functional requirements of the early warning system, including real-time monitoring, automatic alarm, and data recording; Select monitoring parameters according to the operating characteristics of the equipment; Determine the warning level. Warnings are divided into different levels, and corresponding handling measures and notification methods are set for each level.

3. The abnormal warning method based on the equipment operation status according to claim 1 is characterized in that: The specific process of the hardware installation is as follows: Sensor installation: Install sensors at the preset locations of the equipment to ensure that the installation location of the sensors can accurately reflect the operating status of the equipment; Data acquisition device configuration, select the data acquisition device, and connect the sensor to the data acquisition device; Configure the parameters of the data acquisition equipment, including sampling frequency, data format, etc.; Network connection: connect the data acquisition equipment to the central monitoring system via a wired or wireless network, and perform network testing at the same time. If the test passes, the connection will be made.

4. The abnormal warning method based on the equipment operation status according to claim 3 is characterized in that: The specific process of the network test is as follows: collecting network signal strength information, network transmission speed information and abnormal fluctuation information; Analyze the network signal strength information. If the network signal strength information is less than the preset value for more than the preset time, it means the test fails. Otherwise, it means it passes. Analyze the network transmission speed information. If the network transmission speed information is less than the preset value for a preset period of time, it means the test fails. Otherwise, it means it passes. Abnormal fluctuation information means that the network fluctuation is greater than the preset value. The number of times the abnormal fluctuation information occurs within the preset time length is extracted. When the number of times the abnormal fluctuation information occurs is greater than the preset value, it means that the test fails, otherwise it means that the test passes.

5. The abnormal warning method based on the equipment operation status according to claim 1 is characterized by: The specific process of step three is as follows: The data acquisition device collects sensor data in real time at a set frequency; The collected data includes equipment operating parameters and equipment status information; The collected data is transmitted to the central monitoring system through the network. During the data transmission process, encryption technology is used to encrypt the data; The encrypted data is stored in the database.

6. The abnormal warning method based on the equipment operation status according to claim 1 is characterized by: The specific process of data analysis and modeling of the collected data is as follows: Data preprocessing: clean the collected data to remove noise data and outliers; Standardize the data to make it meet the requirements of the analysis model; Establish a normal operation model and collect historical data of the equipment during normal operation as sample data; Use machine learning algorithms or statistical analysis methods to establish a model of the normal operating status of the equipment. The model can describe the parameter change pattern of the equipment during normal operation. Establish an anomaly detection model. According to the physical characteristics and operating rules of the equipment, establish an anomaly detection model. The model can identify abnormal changes in the operating status of the equipment.

7. The abnormal warning method based on the equipment operation status according to claim 1 is characterized by: The specific process of establishing the early warning mechanism and issuing early warning notifications is as follows: Set warning thresholds based on the equipment's operating parameters and historical data; The threshold is a dynamic threshold or a fixed threshold; Real-time monitoring and analysis: receiving equipment operation data in real time and analyzing it through anomaly detection models; When the equipment's operating status is abnormal, the system immediately triggers an early warning.

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