CN109826816B - Intelligent early warning system and method for fan stall - Google Patents
Intelligent early warning system and method for fan stall Download PDFInfo
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- CN109826816B CN109826816B CN201811642240.6A CN201811642240A CN109826816B CN 109826816 B CN109826816 B CN 109826816B CN 201811642240 A CN201811642240 A CN 201811642240A CN 109826816 B CN109826816 B CN 109826816B
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Abstract
本发明公开了一种风机失速智能预警系统与方法。本发明包括信号采集与预处理模块、数据计算模块、智能分析模块、预警报警模块；信号采集与预处理模块，用于从自动控制系统采集风机运行的当前时间的前的一段时间T内的历史数据，并对历史数据进行分析和处理，得到有价值的历史数据曲线信息；数据计算模块，用于计算数据变化趋势，并根据信号周期获得可靠的实时数据值及其数据偏差；智能分析模块，用于根据数据变化趋势、实时数据值、数据偏差等评估风机失速的倾向性或失速状态；预警报警模型，用于在风机出现失速倾向或已发生失速时，提供预警或报警信息。本发明在风机完全失速前数分钟进行预警，为消除失速提供充足的干预时间。
The invention discloses a fan stall intelligent early warning system and method. The invention includes a signal acquisition and preprocessing module, a data calculation module, an intelligent analysis module, and an early warning and alarm module; the signal acquisition and preprocessing module is used to collect the history within a period of time T before the current time of fan operation from the automatic control system data, and analyze and process the historical data to obtain valuable historical data curve information; the data calculation module is used to calculate the data trend, and obtain reliable real-time data values and data deviations according to the signal cycle; the intelligent analysis module, It is used to evaluate the tendency of the fan to stall or the stall state according to the data change trend, real-time data value, data deviation, etc.; the early warning alarm model is used to provide early warning or alarm information when the fan has a stall tendency or has stalled. The present invention provides an early warning several minutes before the fan is completely stalled, and provides sufficient intervention time for eliminating the stall.
Description
Technical Field
The invention belongs to the technical field of production safety, and relates to an intelligent early warning system and method for fan stall.
Background
When a fan is in a normal working condition, an attack angle is small (an included angle between an airflow direction and a blade chord of the blade is an attack angle), airflow bypasses an airfoil-shaped blade to keep a streamline state, when the airflow forms a positive attack angle with an inlet of the blade, namely α is greater than 0, and the positive attack angle exceeds a certain critical value, the flow working condition of the back of the blade begins to deteriorate, a boundary layer is damaged, a vortex region appears at the tail end of the back of the blade, namely a 'stalling' phenomenon is generated.
Disclosure of Invention
In view of the limitations of the prior art and the development trend of the intelligent technology, the invention provides an intelligent early warning system and method for the stall of a fan.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the system comprises a signal acquisition and preprocessing module, a data calculation module, an intelligent analysis module and an early warning and alarming module.
The signal acquisition and preprocessing module is used for acquiring historical data in a period of time T before the current time of fan operation from the automatic control system, analyzing and processing the historical data to obtain valuable historical data curve information, and is specifically realized as follows:
historical data in a period of time T before the current time of fan operation are collected from an automatic control system, wherein the historical data comprise: fan current, movable vane opening, fan flow, inlet and outlet differential pressure. The length of the time period T and the time interval of the data points are set according to the data characteristics, generally, the time period T is 1-10 minutes, and the shorter the time period is, the more sensitive the reaction is; the time interval is generally 1 second and needs to be comprehensively determined according to the data frequency and the data processing speed requirement of the automatic control system. And smoothing the collected historical data by using average moving or other mathematical methods, eliminating noise signals of a historical data curve, and removing abnormal values. And calculating the data fluctuation period of the preprocessed historical data by a mathematical method.
The data calculation module is used for calculating a data change trend (derivative) and obtaining a reliable real-time data value and a data deviation thereof according to a signal period, and specifically comprises the following steps:
according to the data fluctuation period obtained by the signal acquisition and preprocessing module, averaging the preprocessed historical data according to the period to obtain a stable curve reflecting the real data characteristics, and then respectively calculating the change slope of the periodic average curve of the fan current, the movable blade opening, the fan flow and the inlet-outlet differential pressure by using a derivative method. And calculating the average value of the last period according to the data fluctuation period as a real-time data value, and calculating the variation of historical data within one minute, namely data deviation, including fan current, fan flow and inlet-outlet differential pressure.
The intelligent analysis module is used for evaluating the tendency or stall state of the fan stall according to data change trend, real-time data values, data deviation and the like, and specifically comprises the following steps:
diagnosis of the tendency of the fan to stall: and judging whether the fan current, the fan flow, the inlet-outlet differential pressure and the movable vane opening degree exceed respective limit values or not according to the fan current, the fan flow, the inlet-outlet differential pressure and the change slope of the movable vane opening degree respectively, and if the fan current, the fan flow, the inlet-outlet differential pressure and the movable vane opening degree exceed the respective limit values, judging that the fan has stall tendency. The overall logic is: if the opening degree of the movable blade continuously increases or reaches the maximum value, the current of the fan, the flow of the fan and the inlet-outlet differential pressure are continuously reduced, and the respective change speeds exceed respective limit values, the stalling is indicated to be imminent. The variation limit value of each parameter is determined by the operation characteristics of a specific fan, big data analysis is needed according to historical data, and then supplement and verification are carried out through necessary tests.
And (3) diagnosing a stalling state of the fan: and establishing a relation model between the fan current, the fan flow, the inlet-outlet pressure difference and the movable blade opening degree by utilizing the big data, wherein the relation model comprises an average value relation and a normal region relation, for example, the fan runs under 70% of movable blade opening degree, and the fan current average value Av and the fluctuation range As of normal running under the movable blade opening degree in the historical big data are calculated. The overall logic is: if the opening degree of the movable blades is continuously increased or reaches the maximum value, the real-time values of the fan current, the fan flow and the inlet-outlet differential pressure respectively deviate from the minimum value ranges (Av-As, Fv-Fs and Pv-Ps) of the fan current, the fan flow and the inlet-outlet differential pressure corresponding to the real-time values of the opening degree of the movable blades, and the fan is in the stall state.
The early warning and alarming model is used for providing early warning or alarming information when the fan has a stalling tendency or has stalled.
When the fan has a stall tendency, performing stall early warning, wherein the early warning color is yellow;
and when the fan is in a stall state, performing stall alarm, wherein the alarm color is red.
The early warning and alarming modes comprise sound, light, pop-up frames and other striking modes.
The invention has the following beneficial effects:
according to the method, the key parameters of the stall of the fan are preprocessed through a computer intelligent analysis technology to obtain effective signal information, further obtain the effective change trend of the key parameters, and then obtain the trend relation of related parameters of the stall of the fan through historical big data analysis and fan characteristic analysis (possibly needing tests), so that the tendency of the stall of the fan is analyzed in real time, early warning can be performed minutes before the fan completely stalls, and sufficient intervention time is provided for stall elimination.
Drawings
FIG. 1 is a flow chart of the system and method of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, the intelligent early warning system and method for fan stall includes a signal acquisition and preprocessing module ①, a data calculation module ②, an intelligent analysis module ③, and an early warning module ④.
The signal collecting and preprocessing module ① is used for collecting historical data of the fan in a period of time before the current time of operation from the automatic control system, and analyzing and processing the data signal to obtain valuable historical data curve information.
The data calculating module ② is used to calculate the data variation trend (derivative) and obtain reliable real-time data and its variation amplitude (data deviation) according to the signal period.
The intelligent analysis module ③ is used for estimating the tendency of the fan stall or the stall state according to the data change trend, the real-time value, the data deviation and the like.
The early warning model ④ is used for providing early warning or warning information when the fan is in a stalling tendency or has stalled.
The signal collection and preprocessing module ① collects historical data from the automatic control system in a period of time before the current time of fan operation, wherein the data includes fan current, movable blade opening, fan flow, inlet and outlet differential pressure, the length of the time period and data point time interval are set according to data characteristics, generally, the time period is 1-10 minutes, the shorter the time period is, the more sensitive the reaction is, the time interval is generally 1 second, and the time interval is determined comprehensively according to the data frequency of the control system and the data processing speed requirement.
The data calculation module ② averages the preprocessed historical data according to the period according to the obtained data change period to obtain a stable curve representing the characteristics of real data, and then calculates the slope of the periodic average curve of the fan current, the movable vane opening, the fan flow and the inlet-outlet differential pressure by using a derivative method, and calculates the average value of the last period according to the data period as a real-time value, and calculates the variation of the data within one minute, namely the data deviation, including the fan current, the fan flow and the inlet-outlet differential pressure.
The intelligent analysis module ③ judges whether the fan stall tendency exceeds the respective limit value according to the change slopes of the fan current, the fan flow, the inlet-outlet differential pressure and the movable vane opening, if the change slopes exceed the respective limit values, the fan stall tendency is judged, the general logic is that the fan stall tendency is about to occur if the movable vane opening continuously increases or reaches the maximum value, the fan current, the fan flow and the inlet-outlet differential pressure continuously decrease, and the respective change speeds exceed the respective limit values, the sizes of the change limit values of the parameters are determined by the operation characteristics of the specific fan, large data analysis is needed according to historical data, and then supplement and verification are carried out through necessary tests.
The intelligent analysis module ③ diagnoses the stall state of the fan, and establishes a relationship model between the fan current, the fan flow, the inlet-outlet differential pressure and the rotor blade opening degree by using big data, wherein the relationship model comprises a mean value relationship and a normal region relationship, for example, the fan operates under 70% of the rotor blade opening degree, and the general logic of calculating the fan current mean value Av and the fluctuation range As. of the normal operation under the rotor blade opening degree in the historical big data at the moment is that if the rotor blade opening degree is continuously increased or reaches the maximum value, the real-time values of the fan current, the fan flow and the inlet-outlet differential pressure respectively deviate from the minimum value ranges (Av-As, Fv-Fs and Pv-Ps) of the fan current, the fan flow and the inlet-outlet differential pressure corresponding to the rotor blade opening degree real-time values, and the fan is.
The early warning module ④ performs stall early warning when the fan has a stall tendency, the early warning color is yellow, and performs stall warning when the fan is in a stall state, the warning color is red.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111985096B (en) * | 2020-08-12 | 2023-09-15 | 浙江浙能技术研究院有限公司 | Draught fan stall intelligent early warning method based on actual critical stall curve of draught fan |
| CN112132485A (en) * | 2020-09-30 | 2020-12-25 | 上海众源网络有限公司 | An index data processing method, device, electronic device and storage medium |
| CN114064760B (en) * | 2021-11-18 | 2022-12-13 | 广州泰禾大数据服务有限公司 | Multi-dimensional early warning analysis and judgment method for data |
| CN114738308B (en) * | 2022-04-28 | 2024-01-23 | 中国华能集团清洁能源技术研究院有限公司 | A fan failure early warning method and system that can locate specific equipment |
| CN115013340B (en) * | 2022-05-19 | 2024-08-06 | 西安热工研究院有限公司 | Early warning method and device for adjusting blade failure of axial flow fan in thermal power plant |
| CN114992150B (en) * | 2022-05-19 | 2024-07-26 | 西安热工研究院有限公司 | Coal-fired power plant fan stall warning method, device and storage medium |
| CN115596696A (en) * | 2022-10-28 | 2023-01-13 | 西安热工研究院有限公司（Cn） | Real-time online estimation method for running state of fan based on data mining |
| CN119358906A (en) * | 2024-09-29 | 2025-01-24 | 内蒙古察哈尔新能源有限公司 | A multifunctional wind farm intelligent monitoring platform |
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| JP2001073992A (en) * | 1999-09-02 | 2001-03-21 | Mitsubishi Heavy Ind Ltd | Stall detecting device for fan |
| JP2004019531A (en) * | 2002-06-14 | 2004-01-22 | Sanyo Electric Co Ltd | Fan |
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| CN1514209A (en) * | 2003-08-01 | 2004-07-21 | 重庆大学 | Method and device for intelligent fault diagnosis of rotating machinery |
| CN102606464A (en) * | 2011-12-15 | 2012-07-25 | 西安兴仪启动发电试运有限公司 | Real-time monitoring and preventing method for surge and stall of axial flow fan |
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