CN113780896A - Health assessment method for hard rock tunneling system - Google Patents
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Abstract
本发明涉及一种硬岩掘进系统健康评估方法，传感技术包括智能传感模块，数据采集模块，远程传输模块，物联网模块，采集到的数据输入到硬岩掘进系统进行健康评估方法，主要步骤为1)根据硬岩掘进智能传感模块选定健康指标信息，进行数据预处理以及归一化处理，2)根据健康评价指标，建立硬岩掘进系统健康评估模型，3)根据选取一段时间下的健康评估指标数据，4)根据指标数据建立新型白化权函数，选择灰类，5)根据层次分析法主观分健康指标权重，根据熵权法客观分指标权重，将二者结合形成主客观分健康指标权重，6)根据灰色聚类法与组合赋权法结合得出综合灰类决策评价其健康等级，与现有技术相比，本发明具有计算简单准确等优点。
The invention relates to a health evaluation method for a hard rock excavation system. The sensing technology includes an intelligent sensing module, a data acquisition module, a remote transmission module, and an Internet of Things module. The collected data is input to the hard rock excavation system for health evaluation. The steps are 1) selecting health index information according to the hard rock excavation intelligent sensing module, and performing data preprocessing and normalization processing; 2) establishing a hard rock excavation system health evaluation model according to the health evaluation index; 3) selecting a period of time according to 4) Establish a new whitening weight function according to the index data, select the gray category, 5) Subjectively divide the health index weight according to the analytic hierarchy process, and objectively divide the index weight according to the entropy weight method, and combine the two to form a subjective and objective According to the weight of the health index, 6) the comprehensive gray class decision is obtained according to the combination of the gray clustering method and the combined weighting method to evaluate the health level. Compared with the prior art, the present invention has the advantages of simple and accurate calculation and the like.
Description
Technical Field
The invention relates to a health assessment method, in particular to a health assessment method for a hard rock tunneling system.
Background
With the rapid development of the coal industry in China, the lip tooth-dependent coal machinery industry is increasingly emphasized, the tunneling and the stoping are important production links of coal mine production, the tunneling machine plays an important role in tunneling, the modern coal machinery is developing towards the direction of no humanization, mechanization and intellectualization, and higher requirements are provided for ensuring the stability and the reliability of tunneling equipment and the health degree of a tunneling system. The health condition of the tunneling equipment directly influences the efficiency and the cost of the whole coal mining system, most of the existing underground coal machinery adopts a manual inspection mode after going into the well, records various health index data, combines the maintenance experience of hard rock tunneling equipment, and replaces required accessories. The traditional maintenance mode can not realize real-time monitoring, is difficult to predict which kind of faults occur under the working state, and lacks an instructive database. Monitoring information of the system is acquired by using an intelligent sensing technology, the monitoring information comprises monitoring information of cutting force, vibration, temperature and the like of hard rock tunneling equipment, the health state of the hard rock tunneling system is evaluated, possible equipment health problems are predicted, and an evaluation result can be used as reference data during equipment state maintenance.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a method for evaluating the health state of a hard rock tunneling system. The data acquisition module is connected with the upper computer and each intelligent sensing module, and the Internet of things module is connected with the upper computer. The method for evaluating the health of the hard rock tunneling system mainly comprises a gray clustering method and a combined weighting method.
The purpose of the invention can be realized by the following technical scheme:
the hard rock tunneling system health assessment method based on the sensing technology comprises the following steps:
1) selecting health index information according to the intelligent hard rock tunneling sensing module, and performing data preprocessing and normalization processing;
2) establishing a health evaluation model of the hard rock tunneling system according to the health evaluation index;
3) selecting health evaluation index data under a period of time;
4) establishing a novel whitening weight function according to the index data, and selecting grey;
5) subjectively dividing the health index weight according to an analytic hierarchy process, and objectively dividing the index weight according to an entropy weight process; combining the two to form subjective and objective health index weight;
6) and obtaining a comprehensive grey decision evaluation health grade according to the combination of a grey clustering method and a combined weighting method.
The step 1) intelligent sensing module comprises an acceleration sensor, a tension and pressure sensor and a temperature sensor, data preprocessing comprises data cleaning and noise reduction, and normalization processing is to determine that the index data range is [0,1 ].
The health assessment model in the step 2) is composed of improved gray clustering and a combined weighting method.
And 3) selecting a period of health index data including the sampling rate, the standard value and the threshold range of the measurement data in the evaluation time.
The weights in the step 5) and the step 6) and the comprehensive grey decision.
And finally, selecting the affiliated health grade according to the maximum membership rule.
Compared with the prior art, the invention has the following advantages.
Firstly, the calculation is simple and accurate: the grey class is divided, different index weights are calculated according to a combined weighting method, the weights are given to grey clustering coefficients to calculate comprehensive decision weights, the health state is obtained, and the evaluation result can supplement the equipment state maintenance data.
And secondly, the health state of the tunneling equipment can be predicted in advance, active maintenance is realized, the cost is reduced, and stable operation is ensured.
Drawings
Fig. 1 is a schematic structural diagram of a sensing technology, which includes a hard rock tunneling system 1, an intelligent sensing module 2, a data acquisition module 3, a remote transmission module 4, and an upper computer 5.
FIG. 2 is a flow chart of the method.
Detailed Description
In order to accurately describe the technical scheme in the embodiment of the invention, the following description is further made in combination with the accompanying drawings in the embodiment of the invention. It is to be understood that the drawings in the description are for purposes of illustration only and are not intended as a definition of the limits of the invention.
The invention is described in detail below with reference to the figures and specific embodiments.
As shown in fig. 1, the main structure of the sensing technology includes a hard rock tunneling system 1, an intelligent sensing module 2, a data acquisition module 3, a remote transmission module 4, and an upper computer 5. The intelligent sensing module 2 comprises an acceleration sensor, a tension pressure sensor and a temperature sensor, wherein the acceleration sensor is connected with a main shaft of the heading machine, the tension pressure sensor is in contact with the surface of hard rock cut by the heading machine, and the temperature sensor is connected with a hydraulic pump.
As shown in fig. 2, the method for evaluating the health of a hard rock tunneling system based on a sensing technology specifically comprises the following steps.
Analyzing index information acquired by the operation of the equipment of the tunneling system, acquiring related data of the health state of the tunneling system, dividing the data into health, sub-health, slight faults, faults and serious faults by using a gray clustering method, carrying out weight division on different indexes according to a combined weighting method, and finally combining the health state and the serious faults to calculate and obtain the corresponding health state of the tunneling system according to a maximum membership principle.
And acquiring the data sampling rate of the equipment operation amount according to the health index according to the evaluation model, and setting an operation threshold value.
The method comprises the following steps of carrying out data cleaning and noise reduction on the acquired health index data: setting a data standard value, carrying out reasonable checking calculation, comparing with healthy data, setting a data index value range, judging as noise data when the data exceeds the value range, judging as noise data when the data generated during equipment debugging and maintenance periods, and judging as equipment shutdown data when the data generated during the shutdown periods is noise data, abnormal state data and frequently abnormal data during the operation periods are noise data.
And establishing novel whitening weight functions of different health indexes, and dividing different gray classes.
Evaluating and calculating the equipment health index data, wherein the calculation formula is as follows:
where k represents the gray class and represents the turning point of the whitening weight function.
The weighting formula of the combined weighting method is as follows:
in the formula
Representing the jth evaluation index weight of the surface analytic hierarchy process;
representing an entropy weight method to determine the weight of the jth evaluation index;
a specific gravity representing the weight of the jth evaluation index obtained by the analytic hierarchy process;
represents the ratio of the j-th evaluation index weight obtained by the entropy weight method.
And calculating a comprehensive decision coefficient, wherein the calculation formula is as follows:
wherein the equipment health grade is k, the health evaluation index is m, and the whitening weight function is
。
And calculating to obtain a comprehensive decision coefficient, and obtaining a health state evaluation result according to a maximum membership rule.
The above-described embodiments of the present invention do not limit the scope of the present invention. Modifications and equivalents of the above embodiments may be made by those skilled in the art without departing from the scope of the present disclosure.
Claims (6)
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118863672A (en) * | 2024-09-25 | 2024-10-29 | 山东新矿信息技术有限公司 | Method, system, equipment and medium for evaluating health status of tunneling robot equipment |
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Non-Patent Citations (1)
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Application publication date: 20211210 |