CN-121980301-A - Method for evaluating service life of on-line monitoring equipment under field operation condition

Abstract

The invention belongs to the technical field of equipment life detection, and discloses a method for evaluating the equipment life on line under the field operation condition, which comprises the following steps of collecting the operation environment parameters and the equipment state parameters of equipment, preprocessing the collected parameter data, and obtaining standardized data; the method comprises the steps of determining health state scores of equipment based on equipment state parameters, calculating basic failure rates of the equipment according to the health state scores, calculating environment stress weights corresponding to environment factors through environment weight functions based on environment parameters in standardized data, fusing the basic failure rates with the environment stress weights to obtain real-time failure rates of the equipment under current environment conditions, calculating residual service lives of the equipment according to the real-time failure rates, and outputting life prediction results based on an accumulated damage model. The invention can change the operation and maintenance mode from fixed preventive maintenance to on-demand maintenance based on accurate prediction, thereby avoiding resource waste and preventing fault risks.

Inventors

• WANG ZHENG
• XU SIBO
• YANG XI
• DAN HANYONG
• ZHANG LIANSHENG
• Chen Yuemu
• LI HAIFENG
• YANG QIJIA
• CHENG MENG
• XU GANGYI
• XU WANGSHENG
• HU JINGBO
• HUANG QIAN
• MENG XIANGLONG
• ZHANG ZHIRAN
• ZHANG HANJIE
• WANG HONGQI

Assignees

• 中国南方电网有限责任公司超高压输电公司贵阳局

Dates

Publication Date

20260505

Application Date

20251209

Claims (10)

1. 1. A method for evaluating the life of on-line monitoring equipment under field operating conditions, comprising the steps of: collecting running environment parameters and equipment state parameters of equipment; determining a health state score of the device based on the device state parameter, and calculating a basic failure rate of the device according to the health state score; Calculating the environmental stress weight corresponding to each environmental factor through an environmental weight function based on the environmental parameters in the standardized data; fusing the basic failure rate with each environmental stress weight to obtain the real-time failure rate of the equipment under the current environmental condition; And calculating the residual service life of the equipment according to the real-time failure rate, and outputting a life prediction result based on the accumulated damage model.
2. 2. The method of claim 1, wherein the operating environment parameters include climate environment parameters, geographic environment parameters and equipment operating condition parameters; Preprocessing the collected various parameter data comprises data integrity verification, abnormal data identification and correction, data standardization processing and missing data filling.
3. 3. The method of claim 1, wherein the status parameters of the equipment are monitored by monitoring at least one of operational performance parameters, functional output parameters and system self-test parameters; And comparing the state parameter with a reference value in an ideal state, and calculating a score between 0 and 100 through a preset scoring rule.
4. 4. A method of assessing the longevity of on-line monitoring equipment under field operating conditions according to claim 1 or 3, wherein the base failure rate is calculated by the formula: ; Wherein, the Real-time failure rate of the equipment under standard environmental conditions; scoring the health status of the equipment, and fully dividing the equipment into 100 points; Is a proportionality coefficient and reflects the type characteristics of equipment; To reflect the degree of curvature of the state change on the failure rate.
5. 5. A method of assessing a longevity of an on-line monitoring device under field operating conditions in accordance with claim 4, the environment weight function is characterized in that the environment weight function is calculated by adopting the following formula: ; Wherein, the Is the first Actual measurement values of individual environmental factors; Is the first Standard operating condition values (equipment rated environmental conditions) of individual environmental factors; is an environmental sensitivity coefficient.
6. 6. The method of claim 5, wherein the real-time failure rate is calculated by the formula: ; ; corrected real-time failure rate: ; Wherein, the As a term for the correction of the environmental stress, Is a real-time failure rate.
7. 7. The method of assessing the longevity of an on-line monitoring device under field operating conditions of claim 6, wherein the remaining useful life is calculated by the formula: ; The ratio relation with the expected health life in standard environment is: ; Wherein, the The expected remaining life for consideration of health and environmental impact.
8. 8. The method of assessing the longevity of an on-line monitoring device under field operating conditions of claim 7, wherein the cumulative damage model is calculated by the formula: ; ); in order to accumulate the degree of damage, In order to achieve a remaining useful life of the device, For the expected health life of the device in an ideal health state, the integration interval is from the device commissioning to the current time.
9. 9. The method for evaluating the life of an on-line monitoring device under field operating conditions of claim 5, wherein said environmental sensitivity factor The method is determined by an accelerated aging test and specifically comprises the following steps of: a reference test step, namely carrying out life test on the online monitoring equipment under standard environmental conditions, and determining a proportionality coefficient K and a curvature coefficient C in the basic fault rate model by a statistical analysis method; The single factor acceleration test step comprises fixing other environmental factors as standard working condition values, and changing the ith environmental factor only Is used to determine the stress level of the (c) in the (c), at least three different stress levels Respectively performing accelerated aging tests, and recording real-time fault rate data of equipment under each stress level ; A parameter fitting step of obtaining a basic failure rate based on the reference test And real-time fault rate data obtained by the single factor acceleration test The environmental sensitivity coefficient estimated value corresponding to each stress level is calculated by the following formula, ; And performing statistical processing on the multiple estimated values to obtain a final environmental sensitivity coefficient 。
10. 10. The method of claim 7, further comprising environmental modification of the actual state duration to transform the actual state duration Tp (S) of the device to an equivalent state duration Tp0 (S, E) on the scale of the expected healthy life T 0 taking into account environmental effects, the transformation formula being: ; Wherein, the For the actual duration of the device under the health status score S, In order to take into account the equivalent duration after the environmental impact, For the expected remaining life in the current state and environment, For the expected health life of the device in an ideal health state, C is the coefficient of curvature.

Description

Method for evaluating service life of on-line monitoring equipment under field operation condition Technical Field The invention relates to the technical field of equipment life detection, in particular to a method for evaluating the life of on-line monitoring equipment under field operation conditions. Background With the rapid development of the electric power industry and the deep promotion of smart grid construction in China, the overhead transmission line is used as an important carrier for electric power transmission, and the safe and reliable operation of the overhead transmission line is directly related to the national energy safety and economic and social development. In recent years, online monitoring devices such as overhead transmission line image video monitoring devices and meteorological sensors are widely applied to power grid systems and used for monitoring fault hidden troubles such as line icing, foreign matter collision, equipment heating and the like in real time. These devices are often deployed in severe field environments such as mountains, coasts, deserts, etc., where reliability and service life pose serious challenges. Therefore, the method accurately predicts the service life of the online monitoring equipment in the real running environment, and has important significance for making scientific operation and maintenance strategies, reducing the cost of the whole life cycle and ensuring the safe and stable running of the power grid. However, there are significant shortcomings to the widely adopted equipment life prediction techniques currently on the market. The traditional periodic maintenance strategy based on time only carries out equipment replacement according to the recommended standard service life of manufacturers, so that the difference of actual operation environments is completely ignored, the equipment fails in advance in a severe environment, and the equipment resource waste is caused in a good environment. The existing simplified model based on single environmental parameter correction considers individual factors such as temperature, but cannot comprehensively reflect comprehensive influence of a complex field environment, and has limited prediction accuracy. Empirical models based in part on historical failure statistics, while capable of providing a mean life reference, lack a targeted analysis of specific environmental conditions, and are not adaptable to significant environmental differences in different regions. Disclosure of Invention In order to overcome the defects in the prior art, the invention aims to provide a method for on-line monitoring equipment service life under the estimated field operation condition, which comprehensively considers the synergistic effect of multiple environmental factors, can adapt to the field complex operation condition, has high prediction precision and can guide accurate operation and maintenance, so as to solve the problems raised by the background technology. The technical scheme adopted by the invention for solving the technical problems is that the method for evaluating the service life of the on-line monitoring equipment under the field operation condition comprises the following steps: collecting running environment parameters and equipment state parameters of equipment, and preprocessing the collected parameter data to obtain standardized data; determining a health state score of the device based on the device state parameter, and calculating a basic failure rate of the device according to the health state score; Calculating the environmental stress weight corresponding to each environmental factor through an environmental weight function based on the environmental parameters in the standardized data; fusing the basic failure rate with each environmental stress weight to obtain the real-time failure rate of the equipment under the current environmental condition; And calculating the residual service life of the equipment according to the real-time failure rate, and outputting a life prediction result based on the accumulated damage model. As a further improvement of the invention, the operating environment parameters include climate environment parameters, geographic environment parameters and equipment operating condition parameters; The preprocessing of the collected various parameter data comprises data integrity verification, abnormal data identification and correction, data standardization processing and missing data filling. As a further improvement of the present invention, by monitoring status parameters of the device, the status parameters include at least one of a runnability parameter, a functional output parameter, and a system self-test parameter; And comparing the state parameter with a reference value in an ideal state, and calculating a score between 0 and 100 through a preset scoring rule. As a further improvement of the invention, the base failure rate is calculated by the following formula: ; Wherei