Method and system for collecting and monitoring health status data of coking equipment

Abstract

The invention discloses a coking equipment health state data acquisition monitoring method and system, and the system comprises an equipment grading module which is used for dividing coking equipment into a plurality of grades; the data acquisition module comprises a wired online acquisition unit, a bus polling acquisition unit and a wireless sensor and is used for performing data acquisition on the coking equipment of multiple grades; the health monitoring and alarm module is used for setting an alarm initial threshold value, realizing adaptive learning of the alarm threshold value, executing an alarm event merging rule and carrying out special working condition alarm suppression; the hardware self-management module comprises an embedded identifier analysis unit and a visual interaction unit; the embedded identifier analysis unit is used for realizing sensor channel ID binding, channel real-time state monitoring and abnormal channel automatic isolation; according to the system, real-time monitoring and early fault early warning of the health state of the whole life cycle of the coking equipment are realized, the operation reliability of the equipment is improved, and the operation and maintenance cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial equipment fault diagnosis, and more particularly to a coking equipment health state data acquisition and monitoring method and system. BACKGROUND

[0002] In the current booming development of industrial Internet of Things, the quality of data acquisition plays a decisive role in the accuracy of industrial equipment fault diagnosis. Coking production line equipment is not only diverse in type, but also complex in type. The current monitoring mode has many problems: traditional manual inspection faces high safety risks, long detection cycle, and insufficient data continuity; although the existing online monitoring system can effectively monitor large equipment, the cost is high, and it is difficult to cover a large number of small and medium-sized equipment, resulting in a significant gap in the monitoring range; even the deployed online monitoring system also has defects such as easy damage of sensors in harsh working conditions, incomplete data acquisition dimension, and lagging fault early warning; at the same time, for the complex working conditions such as high temperature, high dust, and strong corrosion of coking equipment, there is currently a lack of mature multi-parameter fusion analysis method, making it difficult to realize accurate evaluation of the health state of the equipment. SUMMARY

[0003] In view of the problems in the prior art, the purpose of the present application is to provide a coking equipment health state data acquisition and monitoring method and system, which solves the problems of incomplete data acquisition, lagging fault early warning, and low intelligence in the existing coking equipment monitoring system, realizes real-time monitoring and early fault warning of the health state of the coking equipment throughout its life cycle, improves the reliability of the equipment operation, and reduces the operation and maintenance cost.

[0004] The present application adopts the following technical solutions:

[0005] The present application discloses a coking equipment health state data acquisition and monitoring system, comprising:

[0006] An equipment grading module is used to divide the coking equipment into multiple levels;

[0007] A data acquisition module comprises a wired online acquisition unit, a bus polling acquisition unit, and a wireless sensor, which are used to acquire data from the multiple levels of coking equipment;

[0008] A health monitoring and alarm module is used to set an alarm initial threshold, realize adaptive learning of the alarm threshold, execute alarm event merging rules and special working condition alarm suppression;

[0009] The hardware autonomous management module comprises an embedded identification analysis unit and a visual interactive unit; the embedded identification analysis unit is used for realizing sensor channel unique ID binding (including channel type, installation position, calibration record), channel real-time state monitoring and abnormal channel automatic isolation; the visual interactive unit realizes hierarchical management through a physical layer, a data layer (real-time characteristic value, historical trend curve, health degree score), a decision layer (key indicator dashboard, abnormal report calling).

[0010] The application further discloses a coking equipment health state data acquisition and monitoring method.

[0011] Step S1: equipment grading

[0012] According to production factors, safety and environmental protection factors, maintenance factors, economic factors, standby factors and spare parts factors, the coking equipment is divided into three levels of A / B / C.

[0013] Step S2: differential data acquisition

[0014] The A-level equipment is acquired by wired online acquisition, and full-parameter and high-density monitoring is realized; the B-level equipment is acquired by bus type time-sharing polling acquisition, and key parameter monitoring is realized.

[0015] The C-level equipment is acquired by wireless sensor acquisition, and low-data-density monitoring is realized; in the acquisition process, characteristic calculation and state preliminary judgment are synchronously completed through an edge computing unit.

[0016] Step S3: adaptive data transmission

[0017] The transmission frequency is adjusted according to the equipment health parameter.

[0018] Step S4: health monitoring and alarm

[0019] The alarm initial threshold value is set according to the vibration standard;

[0020] The alarm threshold value is adaptively learned based on the average value of the normal monitoring value of the working condition, and the first-level and second-level alarm values are set.

[0021] The continuous m times and interval less than the preset value alarm are combined into one alarm event.

[0022] The alarm output is inhibited in the initial start period, the pre-shutdown period and the speed transition stage of the rotating equipment.

[0023] Step S5: hardware autonomous management

[0024] The sensor channel unique ID binding, real-time state monitoring and abnormal isolation are realized through the embedded identification analysis; the management state is presented through the hierarchical visual interface, and the hardware fault early discovery and management efficiency are improved.

[0025] Beneficial effects

[0026] 1. Hierarchical classification monitoring of coking equipment

[0027] A hierarchical classification data acquisition method and standard for coking equipment are proposed to solve the problem of resource waste in the traditional "one-size-fits-all" acquisition mode and improve the performance-price ratio and the digital coverage rate of coking equipment.

[0028] 2. Improve system response speed and resource utilization

[0029] Through the feature calculation module integrated by the edge acquisition device, the alarm event merging algorithm and the data transmission scheduling strategy, the present application realizes the following technical effects: improving the real-time response performance of the monitoring system; optimizing the bandwidth resource occupation, reducing the transmission flow compared with the traditional full-quantity data transmission mode; reducing the data storage resource consumption; improving the data transmission efficiency, reducing the redundant data transmission, and improving the success rate of effective data transmission. Through intelligent processing on the edge side, the efficient use of system resources is realized under the premise of ensuring monitoring accuracy, which is especially suitable for large-scale equipment monitoring scenarios in complex network environments in coking plants.

[0030] 3. Reduce the occurrence of false alarms and frequent alarms

[0031] Through the application of relative alarm, alarm event merging, and abnormal state processing rules, the number of invalid alarm events is effectively reduced, and the accuracy of health monitoring alarms is improved.

[0032] 4. Self-management of digital hardware

[0033] Through the self-management mechanism of digital hardware, early detection of sensor channel faults is realized, and the efficiency of device management is improved through visual interaction, reducing maintenance and repair costs. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The figure is a schematic diagram of the coking equipment health state monitoring system of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0036] As shown in the figure, the present application discloses a coking equipment health state data acquisition monitoring system, comprising:

[0037] A device hierarchical module for dividing the coking equipment into multiple levels;

[0038] The data acquisition module comprises a wired online acquisition unit, a bus polling acquisition unit and a wireless sensor, and is used for acquiring data of the coking equipment of multiple levels;

[0039] The health monitoring and alarm module is used for setting alarm initial thresholds, realizing alarm threshold adaptive learning, executing alarm event merging rules and special working condition alarm suppression.

[0040] The hardware autonomous management module comprises an embedded identification analysis unit and a visual interactive unit; the embedded identification analysis unit is used for realizing sensor channel unique ID binding (including channel type, installation position and calibration record), channel real-time state monitoring and abnormal channel automatic isolation; and the visual interactive unit realizes hierarchical management through a physical layer, a data layer (real-time characteristic value, historical trend curve and health degree score), and a decision layer (key indicator dashboard and abnormal report calling).

[0041] The coking equipment is divided into three levels of A / B / C, wherein the A-level is a key production equipment, the B-level is a general equipment or a small and medium-sized equipment, and the C-level is a small-sized equipment; the wired online acquisition unit is used for monitoring the A-level equipment in a full parameter and high density (data acquisition interval is second-level) mode; the bus polling acquisition unit is used for monitoring the B-level equipment in a time-sharing polling mode; and the wireless sensor acquisition unit is used for monitoring the C-level equipment in a low data density mode.

[0042] The coking equipment is divided into different importance levels, and the monitoring is differentiated according to the different levels; the edge acquisition and transmission of data are realized; the alarm threshold adaptive learning and alarm event merging rules are designed; and the hardware of the digital monitoring system realizes self-management.

[0043] The present application solves the core defects of the existing coking equipment monitoring system, realizes the health monitoring of the equipment in the whole life cycle, and can be widely applied to the complex working condition equipment monitoring in the fields of coking, steel, chemical industry and other heavy industries, and has remarkable practical value and popularization prospect.

[0044] The present application further comprises an edge computing unit, which is used for realizing edge feature calculation (vibration peak value, effective value and kurtosis value operation), equipment state preliminary judgment in the edge device embedded software, and synchronously processing data acquisition and feature calculation. The feature calculation algorithm is deployed in the collection gateway of the A-level equipment to realize real-time operation of the vibration peak value, effective value and kurtosis value; and the simplified version of the feature calculation algorithm is deployed in the collector of the B / C-level equipment to only operate the key indicators (temperature and vibration effective value).

[0045] For A-level equipment (furnace body): wired vibration sensors (installed on the furnace column), temperature sensors (installed on the combustion chamber wall), pressure sensors (installed on the gas pipeline) are deployed, with a collection interval of 1 second; three-way vibration sensors (bearing end), current sensors (motor line end), sound sensors (fan shell) are deployed on the blower, with a collection interval of 1 second;

[0046] For B-level equipment (conveying pump): 1 RS485 bus collector is deployed for every 5 pumps, and 1 temperature sensor (pump body) and 1 vibration sensor (bearing) are installed on each pump, with a collection interval of 1 minute;

[0047] For C-level equipment (valve): 1 LoRa wireless gateway is deployed for every 10 valves, and 1 wireless temperature sensor (valve body) and 1 wireless switch state sensor are installed on each valve, with a collection interval of 10 minutes.

[0048] It also includes a differentiated data collection module that adjusts the collection and transmission frequency according to the equipment health parameters, and the adjustment method is:

[0049] In normal mode, periodic transmission of characteristic trend data, characteristic value sampling transmission frequency F1;

[0050] In abnormal mode, the equipment health parameter value is greater than the alarm threshold, and the complete vibration waveform data is immediately transmitted, with a sampling transmission frequency F2, F2>F1;

[0051] In the continuous abnormal mode, the continuous N (N greater than or equal to 3) equipment health parameters are greater than the alarm value. The original waveform data transmission amount is set to N, and no waveform is transmitted after exceeding, and the sampling transmission frequency of the characteristic value is F2;

[0052] Regardless of the mode, a set of original vibration waveform data is transmitted daily for comparative analysis.

[0053] The channel real-time state includes communication quality and hardware health, and the visualization interaction unit displays the topology graph and color-coded state through the physical layer, i.e. sensor deployment, the monitoring parameters of the hardware health include sensor bias voltage and drift, and the color-coded state includes green for normal, yellow for warning, and red for failure.

[0054] For the rotating equipment such as ethanol pump and methanol pump in the coking system, the alarm suppression mechanism is automatically activated in the following stages, (1) the initial start, within 3 minutes after the equipment is powered on; (2) the early shutdown stage, within three minutes after receiving the shutdown instruction; (3) the speed transition stage, in the transition process from one speed to another. The suppression mechanism execution logic only collects and stores vibration acceleration, speed and other parameters, and does not trigger alarm event output temporarily, and automatically releases the suppression after the working condition is stable.

[0055] The application further discloses a coking equipment health state data acquisition and monitoring method, comprising the following steps:

[0056] Step S1: equipment classification

[0057] Based on production factors, safety and environmental protection factors, maintenance factors, economic factors, standby factors and spare parts factors, the coking equipment is divided into three levels of A / B / C; for the above three levels, the A level is a key production equipment, the B level is a general equipment or a small and medium-sized equipment, and the C level is a small-sized equipment;

[0058] Step S2: differential data acquisition

[0059] The A level equipment is collected by a wired online collection, and full parameter and high density monitoring is realized; the B level equipment is collected by a bus type time sharing polling collection, and key parameter monitoring is realized;

[0060] The C level equipment is collected by a wireless sensor, and low data density monitoring is realized; during the collection process, feature calculation (vibration peak value, effective value and kurtosis value operation) and state preliminary judgment are synchronously completed by an edge calculation unit;

[0061] Step S3: adaptive data transmission

[0062] The transmission frequency is adjusted according to the equipment health parameters (single feature index or multi-feature index weighted operation value);

[0063] Step S4: health monitoring and alarm

[0064] The alarm initial threshold value is set according to the vibration standard of ISO10816-3;

[0065] The alarm threshold value is adaptively learned based on the average value of the normal working condition monitoring value, and the first level (2-3 times of the reference value) and the second level (4-5 times of the reference value) alarm values are set;

[0066] The continuous m times (m>2) and interval less than the preset value alarms are combined into one alarm event;

[0067] The alarm output is suppressed for the initial start, the early shutdown stage and the speed transition stage of the rotating equipment;

[0068] Step S5: Hardware autonomous management

[0069] The application realizes sensor channel unique ID binding, real-time state monitoring and abnormal isolation through embedded identification analysis; the management state is presented through a layered visual interface, and early detection of hardware faults and improvement of management efficiency are realized.

[0070] The application realizes hierarchical monitoring, cost reduction and efficiency improvement: through device grading and differential collection, the problem of resource waste in the traditional "one-size-fits-all" mode is solved (such as C-class equipment does not need to deploy high-priced wired systems), the digital coverage rate of coking equipment is improved, and the overall cost of the monitoring system is reduced;

[0071] Edge intelligence, optimize resources: the edge computing unit realizes "collection - processing" synchronization, reduces transmission traffic compared with the traditional full data transmission mode, reduces bandwidth and storage resource consumption; real-time feature calculation and state judgment improve system response speed, and early fault identification time is greatly shortened;

[0072] Precise early warning, reduce false alarms: through threshold adaptive learning (adapt to different working conditions), alarm event merging (avoid frequent alarms), special working condition suppression (avoid start-stop false alarms), the number of invalid alarm events is greatly reduced, and the work efficiency of operation and maintenance personnel is improved;

[0073] Hardware autonomy, guarantee reliability: the hardware autonomous management mechanism realizes early detection of sensor channel faults, and the visual interaction greatly improves the hardware maintenance efficiency, reduces the diagnosis failure rate caused by hardware faults.

[0074] In step S2, the full parameters include vibration, temperature, current, sound, pressure, process parameters, and the collection interval of high-density monitoring is second-level;

[0075] The key parameters include temperature and vibration, and the interval of time-sharing polling collection is minute-level;

[0076] The interval of low-data-density monitoring is 10-minute level.

[0077] The above describes only the preferred specific embodiments of the application; however, the protection scope of the application is not limited thereto. Any skilled person in the art can make equivalent substitutions or changes to the technical solutions and improvement concepts of the application within the technical range disclosed by the application, which should be covered within the protection scope of the application.

Claims

1. A coking plant health status data acquisition monitoring system, characterized in that: Comprise: A device grading module for dividing the coking device into multiple levels; A data acquisition module comprising a wired online acquisition unit, a bus polling acquisition unit and a wireless sensor, the wired online acquisition unit, the bus polling acquisition unit and the wireless sensor being used for data acquisition of the multiple levels of the coking device; A health monitoring and alarm module for setting alarm initial threshold, realizing alarm threshold self-adaptive learning, executing alarm event merging rules and special working condition alarm suppression; A hardware autonomous management module comprising an embedded identification analysis unit and a visual interactive unit; the embedded identification analysis unit is used for realizing sensor channel unique ID binding, channel real-time state monitoring and abnormal channel automatic isolation; the visual interactive unit realizes hierarchical management through physical layer, data layer and decision layer.

2. The coking plant health state data acquisition monitoring system according to claim 1, characterized in that: The coking device is divided into A / B / C three levels, wherein the A level is the key production device, the B level is the general device or small and medium-sized device, and the C level is the small device; the wired online acquisition unit is used for full parameter and high density monitoring of the A level device; The bus type polling acquisition unit is used for key parameter monitoring of the B level device in a time-sharing polling manner; the wireless sensor acquisition unit is used for low data density monitoring of the C level device.

3. The coking plant health data acquisition monitoring system of claim 1, wherein: It also comprises an edge computing unit, which is used for realizing edge feature calculation and device state preliminary judgment in the edge device embedded software, and synchronously processing data acquisition and feature calculation.

4. The coking plant health data acquisition monitoring system of claim 1, wherein: It also comprises a differentiated data acquisition module, which adjusts the acquisition and transmission frequency according to the device health parameters, and the adjustment method is as follows: In normal mode, periodically transmit feature trend data, and the sampling transmission frequency of feature value is F1; In abnormal mode, the device health parameter value is greater than the alarm threshold, and the complete vibration waveform data is immediately transmitted, the sampling transmission frequency of feature value is F2, and F2>F1; In the continuous abnormal mode, the continuous N device health parameters are greater than the alarm value. The original waveform data transmission amount is set to N, and no waveform is transmitted after exceeding, and the sampling transmission frequency of feature value is F2; No matter what kind of mode, a set of original vibration waveform data is transmitted daily for comparative analysis.

5. The coking plant health data acquisition monitoring system of claim 1, wherein: The channel real-time state includes communication quality and hardware health degree, the visual interactive unit realizes sensor deployment topology and color coding state through physical layer, the monitoring parameters of the hardware health degree include sensor bias voltage and drift amount, and the color coding state includes green for normal, yellow for early warning and red for fault.

6. A method for collecting and monitoring the health status data of a coking plant, characterized in that: Comprise the following steps: Step S1: device grading The coking device is divided into A / B / C three levels according to production factors, safety and environmental protection factors, maintenance factors, economic factors, standby factors and spare parts factors; Step S2: differentiated data acquisition The A level device is acquired by wired type online acquisition to realize full parameter and high density monitoring; the B level device is acquired by bus type time-sharing polling acquisition to realize key parameter monitoring; The C level device is acquired by wireless sensor acquisition to realize low data density monitoring, and feature calculation and state preliminary judgment are synchronously completed by the edge computing unit during the acquisition process; Step S3: adaptive data transmission Adjusting transmission frequency according to equipment health parameters; Step S4: Health monitoring and alarm Set alarm initial threshold according to vibration standard; Adaptive learning of alarm threshold based on average of normal working condition monitoring values, set primary and secondary alarm values; Merge alarms of continuous m times and interval less than preset value into one alarm event; Suppress alarm output during start-up initial stage, pre-shutdown stage and speed transition stage of rotating equipment; Step S5: Hardware autonomous management Realize sensor channel unique ID binding, real-time state monitoring and abnormal isolation through embedded identification analysis; realize early discovery of hardware faults and improvement of management efficiency through hierarchical visual interface to present management state.

7. The coking plant health status data acquisition monitoring method of claim 6, wherein: In step S2, full parameters include vibration, temperature, current, sound, pressure, process parameters, and the collection interval of high-density monitoring is seconds; Key parameters include temperature and vibration, and the interval of time-sharing polling collection is minutes; The interval of low-data-density monitoring is 10 minutes.

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