CN106253737A - A kind of sliding friction nano generator maintenance system - Google Patents

Abstract

The invention provides a kind of sliding friction nano generator maintenance system, system is obtained including sliding friction nano generator and maintenance policy, described maintenance policy obtains system and includes that data acquisition module, data preprocessing module, risk determine module, maintenance policy generation module, described sliding friction nano generator, including: frictional layer；The conducting element that described frictional layer lower contact is placed；Conductive layer；The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer.Present configuration simply, lightly carries and highly compatible.

Description

A kind of sliding friction nano generator maintenance system

Technical field

Electrical generator fields of the present invention, is specifically related to a kind of sliding friction nano generator maintenance system.

Background technology

In microelectronics and today of material technology high speed development, the most novel have the micro-of several functions and Highgrade integration Type electronic device is continuously developed out, and shows unprecedented application prospect in the every field of people's daily life. But, and the research of power-supply system that these microelectronic devices are mated but relatively lags behind, it is, in general, that these miniature electronics The power supply of device is all directly or indirectly to come from battery.Battery not only volume is relatively big, heavier mass, and contain poisonous Environment and human body are existed potentially hazardous by chemical substance.Therefore, it is developed to the mechanical energy of the naturally occurrings such as motion, vibration The technology being converted into electric energy is extremely important.

In electromotor maintenance technique, typically by assembly is monitored, it is determined whether need repairing, the maintenance plan of formulation Do not have sequencing and the time range of modular repair of assignment component maintenance in slightly, be easily caused the delay because of modular repair Cause transformer fault.

Summary of the invention

For solving the problems referred to above, it is desirable to provide a kind of sliding friction nano generator maintenance system.

The purpose of the present invention realizes by the following technical solutions:

A kind of sliding friction nano generator maintenance system, obtains system including sliding friction nano generator and maintenance policy System, described maintenance policy obtains system and includes that data acquisition module, data preprocessing module, risk determine module, maintenance policy Generation module, described sliding friction nano generator, including:

Frictional layer；

The conducting element that described frictional layer lower contact is placed；

Conductive layer；

The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer；

When externally applied forces make the upper surface of described frictional layer occur with the lower surface of described conductive layer relative sliding friction, And when causing friction area to change, it is possible to export the signal of telecommunication by described conducting element and conductive layer to external circuit.

The invention have the benefit that simple in construction, lightly carry and highly compatible.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain according to the following drawings Other accompanying drawing.

The structural representation of Fig. 1 present invention；

Fig. 2 is the structural representation that maintenance policy obtains system.

Reference:

Maintenance policy acquisition system 1, data acquisition module 11, data preprocessing module 12, risk determine module 13, maintenance Policy generation module 14.

Detailed description of the invention

In conjunction with following application scenarios, the invention will be further described.

Application scenarios 1

See Fig. 1, Fig. 2, a kind of sliding friction nano generator maintenance system of an embodiment of this application scene, bag Include sliding friction nano generator and maintenance policy and obtain system, described maintenance policy obtain system include data acquisition module, Data preprocessing module, risk determine module, maintenance policy generation module, described sliding friction nano generator, including:

Frictional layer；

The conducting element that described frictional layer lower contact is placed；

Conductive layer；

The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer；

When externally applied forces make the upper surface of described frictional layer occur with the lower surface of described conductive layer relative sliding friction, And when causing friction area to change, it is possible to export the signal of telecommunication by described conducting element and conductive layer to external circuit.

Preferably, there is friction electrode between top surface and the lower surface material of described conductive layer of described frictional layer Sequence difference.

This preferred embodiment simplifies structure, provides cost savings, the popularization and application being very beneficial in actual production.

Preferably, described frictional layer is insulant or semi-conducting material.

This preferred embodiment not only serves as mini power source, simultaneously can be used for Electricity Generation.

Preferably, described maintenance policy acquisition system 1 includes data acquisition module 11, data preprocessing module 12, risk Determine module 13, maintenance policy generation module 14；Described data acquisition module 11 is for gathering Monitoring Data according to monitoring policy； Described data preprocessing module 12 is for being normalized pretreatment to Monitoring Data；Described risk determines that module 13 is for determining The degree of risk of assembly；Described maintenance policy generation module 14 is for the degree of risk according to assembly, in conjunction with maintainability and warp Ji sexual factor generates maintenance policy.

This preferred embodiment constructs maintenance policy and obtains the module architectures of system 1.

Preferably, described monitoring policy includes:

(1) determine the monitoring item in each assembly, and monitoring item is divided into general monitoring item and crucial monitoring item；

(2) for typically monitoring item, use wireless sensor network that the health status of monitoring item is monitored and record Health status monitoring amount；

Item is monitored for key, uses the mode that radio sensor network monitoring and personal monitoring combine to monitoring item Health status is monitored, if the wireless senser health status monitoring amount of certain crucial monitoring item is m₁, artificial health status is supervised It is measured as m₂, owing to may be affected by temperature during Sensor monitoring, introduce temperature correction factor ξ, for not by temperature shadow The sensor rung, makes ξ=1, for the sensor of temperature influence,Wherein T is that sensor is monitored Time ambient temperature, T₀For the standard temperature being suitable for during Sensor monitoring, then its final health status monitoring amount m uses following formula true Fixed:

$m=\mathrm{\ξ}\×\left\{\begin{array}{cc}{m}_1,& if\left|m_1-m_2\right|\≤c\\ m_2,& if\left|m_1-m_2\right|>c\end{array}\right.$

In formula, according to c, monitor the constant of item reasonable error range set；

Monitoring item is divided into general monitoring item and crucial monitoring item by this preferred embodiment, and carries out in different ways Monitoring, had both saved monitoring cost, had obtained again monitoring result with a high credibility.

Preferably, health status monitoring amount normalization is expressed as by described data preprocessing module 12:

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount reaches maximum:

$n=1-e^{-\frac{m-L}{H-L}}$

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount minimizes,

$n=1-e^{-\frac{H-m}{H-L}}$

In formula, m represents the original health monitoring variable of a certain monitoring item, and n represents the health after this monitoring item normalization Status monitoring amount, L is the health status monitoring amount lower limit of this monitoring item, and H is the health status monitoring amount upper limit of this monitoring item Value.

The monitoring means taked due to different monitoring projects is different, and the order of magnitude of the monitoring result obtained is different, single Position is also different, and health status monitoring amount is normalized by this preferred embodiment, conveniently assembly is carried out comprehensive assessment.

Preferably, the described degree of risk determining assembly, including:

(1) by the health status monitoring amount weighted average after Monitoring Data normalization various for source, obtain assembly and be good for Health status monitoring index:

$s=\frac{{\Σ}_{i=1}^k{n}_i{w}_i}{{\Σ}_{i=1}^k{w}_i}$

In formula, s represents assembly health status monitoring index, n_iFor i-th monitoring item health status monitoring amount, i=1, 2 ..., k, w_iAccording to each health status monitoring amount n_iThe weight factor that significance level in assembly is arranged；

Set secure threshold T_s, T_s∈ [0.4,0.5], if health status monitoring index s is less than secure threshold T_s, then judge Health status monitoring index s is in exception；

(2) utilize history state of health data and historical failure rate data, set up the failure rate model repaired of equipment:

In formula, r is that equipment can repair fault rate, and s ' is equipment condition monitoring index, and a, b, d are three undetermined constants, According to actual application conditions is different and the corrected parameter that produces；

Wherein, determined the value of parameter a, b, d by historical state data and historical failure rate data, particularly as follows:

If equipment inner assembly number is l, certain assembly z_jAt certain time period T_jThe number of times inside broken down is f_j, its correspondence Health status monitoring index is s_j, by the health status monitoring index s of multiple assemblies_jWith the number of times f broken down_jCollect, Health status monitoring index that then equipment is overall and fault rate computing formula can be repaired be represented by:

$s^{\′}=\frac{{\Σ}_{j=1}^l{s}_j}{l}$

$r=\frac{{\Σ}_{j=1}^l\frac{f_j}{T_j}}{l}$

By above-mentioned health status monitoring index with fault rate iteration can be repaired enter the failure rate model repaired of equipment, thus Determine the value of parameter a, b, d；

(3) according to the failure rate model repaired of equipment, degree of risk X of each assembly is tried to achieve_j:

$X_j=r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& \stackrel{\‾}{s_j},& \mathrm{...},& s_l\end{array}\right)-r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& s_j,& \mathrm{...},& s_l\end{array}\right)$

In formula, j=1 ..., l,Represent and be in abnormal assembly z_jHealth status monitoring index, r (s₁..., s_j..., s_l) represent that each assemblies monitor index is s₁..., s_j..., s_lTime power system can repair fault rate.

This preferred embodiment sets up assembly health status monitoring index and the failure rate model repaired of power system, from And determining the degree of risk of each assembly, it is possible to the more component failure of weighing of science affects journey to what equipment dependability produced Degree, thus be conducive to preferentially keeping in repair for the faulty components that influence degree is big, save maintenance cost, maintenance policy is more than It is decided by the state of assembly itself, and is also dependent upon the component failures impact on equipment dependability, make maintenance policy more objective See and reliable.

Preferably, described combination maintainability and economic factors generate maintenance policy, including:

(1) predefine maintenance policy desired parameters by expert group and this parameter is stored in data base, described maintenance plan Slightly desired parameters includes: each assembly maintenance difficulty M when each monitoring item occurs abnormal_JiWith maintenance economic value E_Ji, the wind of assembly Danger degree X_j, described maintenance difficulty M_JiWith maintenance economic value E_JiShared weight w (X_j)、w(M_Ji)、w(E_Ji), wherein said dimension Repair economic value E_JThe ratio being worth with assembly for maintenance cost；

(2) set the health status monitoring index according to exception and determine that assembly to be repaired is as d_j, j=1 ..., l_d, l_dFor waiting to tie up Repair the number of assembly, according to assembly d to be repaired_jEach exception monitoring item i (i=1,2 ..., k) transfer corresponding maintenance difficulty M_Ji With maintenance economic value E_Ji, calculate comprehensive maintenance difficulty M of assembly to be repaired_Ji' with comprehensive maintenance economic value E_Ji':

${E_{Ji}}^{,}=\underset{i}{\overset{k}{\Σ}}{E}_{Ji}$

(3) the maintenance tendency degree of each assembly to be repaired is calculated

Maintenance tendency degree to each assembly to be repairedSort from big to small, so that it is determined that each assembly to be repaired Maintenance sequencing, i.e. preferential maintenance bigger maintenance tendency degreeCorresponding assembly to be repaired；It addition, according to assembly to be repaired Corresponding comprehensive maintenance difficulty M_Ji' determine corresponding maintenance program, thus generate the maintenance policy of optimum.

This preferred embodiment has formulated the generating mode of optimum maintenance policy, and method is objective simply, the generation of maintenance policy Consider the maintainability in addition to degree of risk and economic factors, add objectivity and reliability that maintenance policy is formulated, And in the face of in a large number wait the assembly to be repaired passed judgment on time, greatly reduce workload, improve work efficiency, and preferably protect Hold the concordance of judge.

In this application scenarios, set secure threshold T_s=0.4, it is relative that the exception of health status monitoring index passes judgment on precision Improve 10%, the reliability of equipment improves 12% relatively.

Application scenarios 2

See Fig. 1, Fig. 2, a kind of sliding friction nano generator maintenance system of an embodiment of this application scene, bag Include sliding friction nano generator and maintenance policy and obtain system, described maintenance policy obtain system include data acquisition module, Data preprocessing module, risk determine module, maintenance policy generation module, described sliding friction nano generator, including:

Frictional layer；

The conducting element that described frictional layer lower contact is placed；

Conductive layer；

The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer；

When externally applied forces make the upper surface of described frictional layer occur with the lower surface of described conductive layer relative sliding friction, And when causing friction area to change, it is possible to export the signal of telecommunication by described conducting element and conductive layer to external circuit.

Preferably, there is friction electrode between top surface and the lower surface material of described conductive layer of described frictional layer Sequence difference.

This preferred embodiment simplifies structure, provides cost savings, the popularization and application being very beneficial in actual production.

Preferably, described frictional layer is insulant or semi-conducting material.

This preferred embodiment not only serves as mini power source, simultaneously can be used for Electricity Generation.

Preferably, described maintenance policy acquisition system 1 includes data acquisition module 11, data preprocessing module 12, risk Determine module 13, maintenance policy generation module 14；Described data acquisition module 11 is for gathering Monitoring Data according to monitoring policy； Described data preprocessing module 12 is for being normalized pretreatment to Monitoring Data；Described risk determines that module 13 is for determining The degree of risk of assembly；Described maintenance policy generation module 14 is for the degree of risk according to assembly, in conjunction with maintainability and warp Ji sexual factor generates maintenance policy.

This preferred embodiment constructs maintenance policy and obtains the module architectures of system 1.

Preferably, described monitoring policy includes:

(1) determine the monitoring item in each assembly, and monitoring item is divided into general monitoring item and crucial monitoring item；

(2) for typically monitoring item, use wireless sensor network that the health status of monitoring item is monitored and record Health status monitoring amount；

Item is monitored for key, uses the mode that radio sensor network monitoring and personal monitoring combine to monitoring item Health status is monitored, if the wireless senser health status monitoring amount of certain crucial monitoring item is m₁, artificial health status is supervised It is measured as m₂, owing to may be affected by temperature during Sensor monitoring, introduce temperature correction factor ξ, for not by temperature shadow The sensor rung, makes ξ=1, for the sensor of temperature influence,Wherein T is that sensor is monitored Time ambient temperature, T₀For the standard temperature being suitable for during Sensor monitoring, then its final health status monitoring amount m uses following formula true Fixed:

$m=\mathrm{\ξ}\×\left\{\begin{array}{cc}{m}_1,& if\left|m_1-m_2\right|\≤c\\ m_2,& if\left|m_1-m_2\right|>c\end{array}\right.$

In formula, according to c, monitor the constant of item reasonable error range set；

Monitoring item is divided into general monitoring item and crucial monitoring item by this preferred embodiment, and carries out in different ways Monitoring, had both saved monitoring cost, had obtained again monitoring result with a high credibility.

Preferably, health status monitoring amount normalization is expressed as by described data preprocessing module 12:

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount reaches maximum:

$n=1-e^{-\frac{m-L}{H-L}}$

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount minimizes,

$n=1-e^{-\frac{H-m}{H-L}}$

In formula, m represents the original health monitoring variable of a certain monitoring item, and n represents the health after this monitoring item normalization Status monitoring amount, L is the health status monitoring amount lower limit of this monitoring item, and H is the health status monitoring amount upper limit of this monitoring item Value.

The monitoring means taked due to different monitoring projects is different, and the order of magnitude of the monitoring result obtained is different, single Position is also different, and health status monitoring amount is normalized by this preferred embodiment, conveniently assembly is carried out comprehensive assessment.

Preferably, the described degree of risk determining assembly, including:

(1) by the health status monitoring amount weighted average after Monitoring Data normalization various for source, obtain assembly and be good for Health status monitoring index:

$s=\frac{{\Σ}_{i=1}^k{n}_i{w}_i}{{\Σ}_{i=1}^k{w}_i}$

In formula, s represents assembly health status monitoring index, n_iFor i-th monitoring item health status monitoring amount, i=1, 2 ..., k, w_iAccording to each health status monitoring amount n_iThe weight factor that significance level in assembly is arranged；

Set secure threshold T_s, T_s∈ [0.4,0.5], if health status monitoring index s is less than secure threshold T_s, then judge Health status monitoring index s is in exception；

(2) utilize history state of health data and historical failure rate data, set up the failure rate model repaired of equipment:

In formula, r is that equipment can repair fault rate, and s ' is equipment condition monitoring index, and a, b, d are three undetermined constants,For The corrected parameter produced according to actual application conditions difference；

Wherein, determined the value of parameter a, b, d by historical state data and historical failure rate data, particularly as follows:

If equipment inner assembly number is l, certain assembly z_jAt certain time period T_jThe number of times inside broken down is f_j, its correspondence Health status monitoring index is s_j, by the health status monitoring index s of multiple assemblies_jWith the number of times f broken down_jCollect, Health status monitoring index that then equipment is overall and fault rate computing formula can be repaired be represented by:

$s^{\′}=\frac{{\Σ}_{j=1}^l{s}_j}{l}$

$r=\frac{{\Σ}_{j=1}^l\frac{f_j}{T_j}}{l}$

By above-mentioned health status monitoring index with fault rate iteration can be repaired enter the failure rate model repaired of equipment, thus Determine the value of parameter a, b, d；

(3) according to the failure rate model repaired of equipment, degree of risk X of each assembly is tried to achieve_j:

$X_j=r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& \stackrel{\‾}{s_j},& \mathrm{...},& s_l\end{array}\right)-r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& s_j,& \mathrm{...},& s_l\end{array}\right)$

In formula, j=1 ..., l,Represent and be in abnormal assembly z_jHealth status monitoring index, r (s₁..., s_j..., s_l) represent that each assemblies monitor index is s₁..., s_j..., s_lTime power system can repair fault rate.

This preferred embodiment sets up assembly health status monitoring index and the failure rate model repaired of power system, from And determining the degree of risk of each assembly, it is possible to the more component failure of weighing of science affects journey to what equipment dependability produced Degree, thus be conducive to preferentially keeping in repair for the faulty components that influence degree is big, save maintenance cost, maintenance policy is more than It is decided by the state of assembly itself, and is also dependent upon the component failures impact on equipment dependability, make maintenance policy more objective See and reliable.

Preferably, described combination maintainability and economic factors generate maintenance policy, including:

(1) predefine maintenance policy desired parameters by expert group and this parameter is stored in data base, described maintenance plan Slightly desired parameters includes: each assembly maintenance difficulty M when each monitoring item occurs abnormal_JiWith maintenance economic value E_Ji, the wind of assembly Danger degree X_j, described maintenance difficulty M_JiWith maintenance economic value E_JiShared weight w (X_j)、w(M_Ji)、w(E_Ji), wherein said dimension Repair economic value E_JThe ratio being worth with assembly for maintenance cost；

(2) set the health status monitoring index according to exception and determine that assembly to be repaired is as d_j, j=1 ..., l_d, l_dFor waiting to tie up Repair the number of assembly, according to assembly d to be repaired_jEach exception monitoring item i (i=1,2 ..., k) transfer corresponding maintenance difficulty M_Ji With maintenance economic value E_Ji, calculate comprehensive maintenance difficulty M of assembly to be repaired_Ji' with comprehensive maintenance economic value E_Ji':

${E_{Ji}}^{,}=\underset{i}{\overset{k}{\Σ}}{E}_{Ji}$

(3) the maintenance tendency degree of each assembly to be repaired is calculated

Maintenance tendency degree to each assembly to be repairedSort from big to small, so that it is determined that each assembly to be repaired Maintenance sequencing, i.e. preferential maintenance bigger maintenance tendency degreeCorresponding assembly to be repaired；It addition, according to assembly to be repaired Corresponding comprehensive maintenance difficulty M_Ji' determine corresponding maintenance program, thus generate the maintenance policy of optimum.

This preferred embodiment has formulated the generating mode of optimum maintenance policy, and method is objective simply, the generation of maintenance policy Consider the maintainability in addition to degree of risk and economic factors, add objectivity and reliability that maintenance policy is formulated, And in the face of in a large number wait the assembly to be repaired passed judgment on time, greatly reduce workload, improve work efficiency, and preferably protect Hold the concordance of judge.

In this application scenarios, set secure threshold T_s=0.42, the exception of health status monitoring index passes judgment on precision phase To improve 9.5%, the reliability of equipment improves 11% relatively.

Application scenarios 3

See Fig. 1, Fig. 2, a kind of sliding friction nano generator maintenance system of an embodiment of this application scene, bag Include sliding friction nano generator and maintenance policy and obtain system, described maintenance policy obtain system include data acquisition module, Data preprocessing module, risk determine module, maintenance policy generation module, described sliding friction nano generator, including:

Frictional layer；

The conducting element that described frictional layer lower contact is placed；

Conductive layer；

The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer；

When externally applied forces make the upper surface of described frictional layer occur with the lower surface of described conductive layer relative sliding friction, And when causing friction area to change, it is possible to export the signal of telecommunication by described conducting element and conductive layer to external circuit.

Preferably, there is friction electrode between top surface and the lower surface material of described conductive layer of described frictional layer Sequence difference.

This preferred embodiment simplifies structure, provides cost savings, the popularization and application being very beneficial in actual production.

Preferably, described frictional layer is insulant or semi-conducting material.

This preferred embodiment not only serves as mini power source, simultaneously can be used for Electricity Generation.

Preferably, described maintenance policy acquisition system 1 includes data acquisition module 11, data preprocessing module 12, risk Determine module 13, maintenance policy generation module 14；Described data acquisition module 11 is for gathering Monitoring Data according to monitoring policy； Described data preprocessing module 12 is for being normalized pretreatment to Monitoring Data；Described risk determines that module 13 is for determining The degree of risk of assembly；Described maintenance policy generation module 14 is for the degree of risk according to assembly, in conjunction with maintainability and warp Ji sexual factor generates maintenance policy.

This preferred embodiment constructs maintenance policy and obtains the module architectures of system 1.

Preferably, described monitoring policy includes:

(1) determine the monitoring item in each assembly, and monitoring item is divided into general monitoring item and crucial monitoring item；

(2) for typically monitoring item, use wireless sensor network that the health status of monitoring item is monitored and record Health status monitoring amount；

Item is monitored for key, uses the mode that radio sensor network monitoring and personal monitoring combine to monitoring item Health status is monitored, if the wireless senser health status monitoring amount of certain crucial monitoring item is m₁, artificial health status is supervised It is measured as m₂, owing to may be affected by temperature during Sensor monitoring, introduce temperature correction factor ξ, for not by temperature shadow The sensor rung, makes ξ=1, for the sensor of temperature influence,Wherein T is that sensor is monitored Time ambient temperature, T₀For the standard temperature being suitable for during Sensor monitoring, then its final health status monitoring amount m uses following formula true Fixed:

$m=\mathrm{\ξ}\×\left\{\begin{array}{cc}{m}_1,& if\left|m_1-m_2\right|\≤c\\ m_2,& if\left|m_1-m_2\right|>c\end{array}\right.$

In formula, according to c, monitor the constant of item reasonable error range set；

Monitoring item is divided into general monitoring item and crucial monitoring item by this preferred embodiment, and carries out in different ways Monitoring, had both saved monitoring cost, had obtained again monitoring result with a high credibility.

Preferably, health status monitoring amount normalization is expressed as by described data preprocessing module 12:

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount reaches maximum:

$n=1-e^{-\frac{m-L}{H-L}}$

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount minimizes,

$n=1-e^{-\frac{H-m}{H-L}}$

In formula, m represents the original health monitoring variable of a certain monitoring item, and n represents the health after this monitoring item normalization Status monitoring amount, L is the health status monitoring amount lower limit of this monitoring item, and H is the health status monitoring amount upper limit of this monitoring item Value.

The monitoring means taked due to different monitoring projects is different, and the order of magnitude of the monitoring result obtained is different, single Position is also different, and health status monitoring amount is normalized by this preferred embodiment, conveniently assembly is carried out comprehensive assessment.

Preferably, the described degree of risk determining assembly, including:

(1) by the health status monitoring amount weighted average after Monitoring Data normalization various for source, obtain assembly and be good for Health status monitoring index:

$s=\frac{{\Σ}_{i=1}^k{n}_i{w}_i}{{\Σ}_{i=1}^k{w}_i}$

In formula, s represents assembly health status monitoring index, n_iFor i-th monitoring item health status monitoring amount, i=1, 2 ..., k, w_iAccording to each health status monitoring amount n_iThe weight factor that significance level in assembly is arranged；

Set secure threshold T_s, T_s∈ [0.4,0.5], if health status monitoring index s is less than secure threshold T_s, then judge Health status monitoring index s is in exception；

(2) utilize history state of health data and historical failure rate data, set up the failure rate model repaired of equipment:

In formula, r is that equipment can repair fault rate, and s ' is equipment condition monitoring index, and a, b, d are three undetermined constants,For The corrected parameter produced according to actual application conditions difference；

Wherein, determined the value of parameter a, b, d by historical state data and historical failure rate data, particularly as follows:

If equipment inner assembly number is l, certain assembly z_jAt certain time period T_jThe number of times inside broken down is f_j, its correspondence Health status monitoring index is s_j, by the health status monitoring index s of multiple assemblies_jWith the number of times f broken down_jCollect, Health status monitoring index that then equipment is overall and fault rate computing formula can be repaired be represented by:

$s^{\′}=\frac{{\Σ}_{j=1}^l{s}_j}{l}$

$r=\frac{{\Σ}_{j=1}^l\frac{f_j}{T_j}}{l}$

By above-mentioned health status monitoring index with fault rate iteration can be repaired enter the failure rate model repaired of equipment, thus Determine the value of parameter a, b, d；

(3) according to the failure rate model repaired of equipment, degree of risk X of each assembly is tried to achieve_j:

$X_j=r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& \stackrel{\‾}{s_j},& \mathrm{...},& s_l\end{array}\right)-r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& s_j,& \mathrm{...},& s_l\end{array}\right)$

In formula, j=1 ..., l,Represent and be in abnormal assembly z_jHealth status monitoring index, r (s₁..., s_j..., s_l) represent that each assemblies monitor index is s₁..., s_j..., s_lTime power system can repair fault rate.

This preferred embodiment sets up assembly health status monitoring index and the failure rate model repaired of power system, from And determining the degree of risk of each assembly, it is possible to the more component failure of weighing of science affects journey to what equipment dependability produced Degree, thus be conducive to preferentially keeping in repair for the faulty components that influence degree is big, save maintenance cost, maintenance policy is more than It is decided by the state of assembly itself, and is also dependent upon the component failures impact on equipment dependability, make maintenance policy more objective See and reliable.

Preferably, described combination maintainability and economic factors generate maintenance policy, including:

(1) predefine maintenance policy desired parameters by expert group and this parameter is stored in data base, described maintenance plan Slightly desired parameters includes: each assembly maintenance difficulty M when each monitoring item occurs abnormal_JiWith maintenance economic value E_Ji, the wind of assembly Danger degree X_j, described maintenance difficulty M_JiWith maintenance economic value E_JiShared weight w (X_j)、w(M_Ji)、w(E_Ji), wherein said dimension Repair economic value E_JThe ratio being worth with assembly for maintenance cost；

(2) set the health status monitoring index according to exception and determine that assembly to be repaired is as d_j, j=1 ..., l_d, l_dFor waiting to tie up Repair the number of assembly, according to assembly d to be repaired_jEach exception monitoring item i (i=1,2 ..., k) transfer corresponding maintenance difficulty M_Ji With maintenance economic value E_Ji, calculate comprehensive maintenance difficulty M of assembly to be repaired_Ji' with comprehensive maintenance economic value E_Ji':

${E_{Ji}}^{,}=\underset{i}{\overset{k}{\Σ}}{E}_{Ji}$

(3) the maintenance tendency degree of each assembly to be repaired is calculated

Maintenance tendency degree to each assembly to be repairedSort from big to small, so that it is determined that each assembly to be repaired Maintenance sequencing, i.e. preferential maintenance bigger maintenance tendency degreeCorresponding assembly to be repaired；It addition, according to assembly to be repaired Corresponding comprehensive maintenance difficulty M_Ji' determine corresponding maintenance program, thus generate the maintenance policy of optimum.

This preferred embodiment has formulated the generating mode of optimum maintenance policy, and method is objective simply, the generation of maintenance policy Consider the maintainability in addition to degree of risk and economic factors, add objectivity and reliability that maintenance policy is formulated, And in the face of in a large number wait the assembly to be repaired passed judgment on time, greatly reduce workload, improve work efficiency, and preferably protect Hold the concordance of judge.

In this application scenarios, set secure threshold T_s=0.45, the exception of health status monitoring index passes judgment on precision phase To improve 9.2%, the reliability of equipment improves 10% relatively.

Application scenarios 4

See Fig. 1, Fig. 2, a kind of sliding friction nano generator maintenance system of an embodiment of this application scene, bag Include sliding friction nano generator and maintenance policy and obtain system, described maintenance policy obtain system include data acquisition module, Data preprocessing module, risk determine module, maintenance policy generation module, described sliding friction nano generator, including:

Frictional layer；

The conducting element that described frictional layer lower contact is placed；

Conductive layer；

The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer；

When externally applied forces make the upper surface of described frictional layer occur with the lower surface of described conductive layer relative sliding friction, And when causing friction area to change, it is possible to export the signal of telecommunication by described conducting element and conductive layer to external circuit.

Preferably, there is friction electrode between top surface and the lower surface material of described conductive layer of described frictional layer Sequence difference.

This preferred embodiment simplifies structure, provides cost savings, the popularization and application being very beneficial in actual production.

Preferably, described frictional layer is insulant or semi-conducting material.

This preferred embodiment not only serves as mini power source, simultaneously can be used for Electricity Generation.

Preferably, described maintenance policy acquisition system 1 includes data acquisition module 11, data preprocessing module 12, risk Determine module 13, maintenance policy generation module 14；Described data acquisition module 11 is for gathering Monitoring Data according to monitoring policy； Described data preprocessing module 12 is for being normalized pretreatment to Monitoring Data；Described risk determines that module 13 is for determining The degree of risk of assembly；Described maintenance policy generation module 14 is for the degree of risk according to assembly, in conjunction with maintainability and warp Ji sexual factor generates maintenance policy.

This preferred embodiment constructs maintenance policy and obtains the module architectures of system 1.

Preferably, described monitoring policy includes:

(1) determine the monitoring item in each assembly, and monitoring item is divided into general monitoring item and crucial monitoring item；

(2) for typically monitoring item, use wireless sensor network that the health status of monitoring item is monitored and record Health status monitoring amount；

Item is monitored for key, uses the mode that radio sensor network monitoring and personal monitoring combine to monitoring item Health status is monitored, if the wireless senser health status monitoring amount of certain crucial monitoring item is m₁, artificial health status is supervised It is measured as m₂, owing to may be affected by temperature during Sensor monitoring, introduce temperature correction factor ξ, for not by temperature shadow The sensor rung, makes ξ=1, for the sensor of temperature influence,Wherein T is that sensor is monitored Time ambient temperature, T₀For the standard temperature being suitable for during Sensor monitoring, then its final health status monitoring amount m uses following formula true Fixed:

$m=\mathrm{\ξ}\×\left\{\begin{array}{cc}{m}_1,& if\left|m_1-m_2\right|\≤c\\ m_2,& if\left|m_1-m_2\right|>c\end{array}\right.$

In formula, according to c, monitor the constant of item reasonable error range set；

Monitoring item is divided into general monitoring item and crucial monitoring item by this preferred embodiment, and carries out in different ways Monitoring, had both saved monitoring cost, had obtained again monitoring result with a high credibility.

Preferably, health status monitoring amount normalization is expressed as by described data preprocessing module 12:

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount reaches maximum:

$n=1-e^{-\frac{m-L}{H-L}}$

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount minimizes,

$n=1-e^{-\frac{H-m}{H-L}}$

In formula, m represents the original health monitoring variable of a certain monitoring item, and n represents the health after this monitoring item normalization Status monitoring amount, L is the health status monitoring amount lower limit of this monitoring item, and H is the health status monitoring amount upper limit of this monitoring item Value.

The monitoring means taked due to different monitoring projects is different, and the order of magnitude of the monitoring result obtained is different, single Position is also different, and health status monitoring amount is normalized by this preferred embodiment, conveniently assembly is carried out comprehensive assessment.

Preferably, the described degree of risk determining assembly, including:

(1) by the health status monitoring amount weighted average after Monitoring Data normalization various for source, obtain assembly and be good for Health status monitoring index:

$s=\frac{{\Σ}_{i=1}^k{n}_i{w}_i}{{\Σ}_{i=1}^k{w}_i}$

In formula, s represents assembly health status monitoring index, n_iFor i-th monitoring item health status monitoring amount, i=1, 2 ..., k, w_iAccording to each health status monitoring amount n_iThe weight factor that significance level in assembly is arranged；

Set secure threshold T_s, T_s∈ [0.4,0.5], if health status monitoring index s is less than secure threshold T_s, then judge Health status monitoring index s is in exception；

(2) utilize history state of health data and historical failure rate data, set up the failure rate model repaired of equipment:

In formula, r is that equipment can repair fault rate, and s ' is equipment condition monitoring index, and a, b, d are three undetermined constants,For The corrected parameter produced according to actual application conditions difference；

Wherein, determined the value of parameter a, b, d by historical state data and historical failure rate data, particularly as follows:

If equipment inner assembly number is l, certain assembly z_jAt certain time period T_jThe number of times inside broken down is f_j, its correspondence Health status monitoring index is s_j, by the health status monitoring index s of multiple assemblies_jWith the number of times f broken down_jCollect, Health status monitoring index that then equipment is overall and fault rate computing formula can be repaired be represented by:

$s^{\′}=\frac{{\Σ}_{j=1}^l{s}_j}{l}$

$r=\frac{{\Σ}_{j=1}^l\frac{f_j}{T_j}}{l}$

By above-mentioned health status monitoring index with fault rate iteration can be repaired enter the failure rate model repaired of equipment, thus Determine the value of parameter a, b, d；

(3) according to the failure rate model repaired of equipment, degree of risk X of each assembly is tried to achieve_j:

$X_j=r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& \stackrel{\‾}{s_j},& \mathrm{...},& s_l\end{array}\right)-r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& s_j,& \mathrm{...},& s_l\end{array}\right)$

In formula, j=1 ..., l,Represent and be in abnormal assembly z_jHealth status monitoring index, r (s₁..., s_j..., s_l) represent that each assemblies monitor index is s₁..., s_j..., s_lTime power system can repair fault rate.

This preferred embodiment sets up assembly health status monitoring index and the failure rate model repaired of power system, from And determining the degree of risk of each assembly, it is possible to the more component failure of weighing of science affects journey to what equipment dependability produced Degree, thus be conducive to preferentially keeping in repair for the faulty components that influence degree is big, save maintenance cost, maintenance policy is more than It is decided by the state of assembly itself, and is also dependent upon the component failures impact on equipment dependability, make maintenance policy more objective See and reliable.

Preferably, described combination maintainability and economic factors generate maintenance policy, including:

(1) predefine maintenance policy desired parameters by expert group and this parameter is stored in data base, described maintenance plan Slightly desired parameters includes: each assembly maintenance difficulty M when each monitoring item occurs abnormal_JiWith maintenance economic value E_Ji, the wind of assembly Danger degree X_j, described maintenance difficulty M_JiWith maintenance economic value E_JiShared weight w (X_j)、w(M_Ji)、w(E_Ji), wherein said dimension Repair economic value E_JThe ratio being worth with assembly for maintenance cost；

(2) set the health status monitoring index according to exception and determine that assembly to be repaired is as d_j, j=1 ..., l_d, l_dFor waiting to tie up Repair the number of assembly, according to assembly d to be repaired_jEach exception monitoring item i (i=1,2 ..., k) transfer corresponding maintenance difficulty M_Ji With maintenance economic value E_Ji, calculate comprehensive maintenance difficulty M of assembly to be repaired_Ji' with comprehensive maintenance economic value E_Ji':

${E_{Ji}}^{,}=\underset{i}{\overset{k}{\Σ}}{E}_{Ji}$

(3) the maintenance tendency degree of each assembly to be repaired is calculated

Maintenance tendency degree to each assembly to be repairedSort from big to small, so that it is determined that each assembly to be repaired Maintenance sequencing, i.e. preferential maintenance bigger maintenance tendency degreeCorresponding assembly to be repaired；It addition, according to assembly to be repaired Corresponding comprehensive maintenance difficulty M_Ji' determine corresponding maintenance program, thus generate the maintenance policy of optimum.

This preferred embodiment has formulated the generating mode of optimum maintenance policy, and method is objective simply, the generation of maintenance policy Consider the maintainability in addition to degree of risk and economic factors, add objectivity and reliability that maintenance policy is formulated, And in the face of in a large number wait the assembly to be repaired passed judgment on time, greatly reduce workload, improve work efficiency, and preferably protect Hold the concordance of judge.

In this application scenarios, set secure threshold T_s=0.48, the exception of health status monitoring index passes judgment on precision phase To improve 9%, the reliability of equipment improves 9% relatively.

Application scenarios 5

See Fig. 1, Fig. 2, a kind of sliding friction nano generator maintenance system of an embodiment of this application scene, bag Include sliding friction nano generator and maintenance policy and obtain system, described maintenance policy obtain system include data acquisition module, Data preprocessing module, risk determine module, maintenance policy generation module, described sliding friction nano generator, including:

Frictional layer；

The conducting element that described frictional layer lower contact is placed；

Conductive layer；

The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer；

When externally applied forces make the upper surface of described frictional layer occur with the lower surface of described conductive layer relative sliding friction, And when causing friction area to change, it is possible to export the signal of telecommunication by described conducting element and conductive layer to external circuit.

Preferably, there is friction electrode between top surface and the lower surface material of described conductive layer of described frictional layer Sequence difference.

This preferred embodiment simplifies structure, provides cost savings, the popularization and application being very beneficial in actual production.

Preferably, described frictional layer is insulant or semi-conducting material.

This preferred embodiment not only serves as mini power source, simultaneously can be used for Electricity Generation.

Preferably, described maintenance policy acquisition system 1 includes data acquisition module 11, data preprocessing module 12, risk Determine module 13, maintenance policy generation module 14；Described data acquisition module 11 is for gathering Monitoring Data according to monitoring policy； Described data preprocessing module 12 is for being normalized pretreatment to Monitoring Data；Described risk determines that module 13 is for determining The degree of risk of assembly；Described maintenance policy generation module 14 is for the degree of risk according to assembly, in conjunction with maintainability and warp Ji sexual factor generates maintenance policy.

This preferred embodiment constructs maintenance policy and obtains the module architectures of system 1.

Preferably, described monitoring policy includes:

(1) determine the monitoring item in each assembly, and monitoring item is divided into general monitoring item and crucial monitoring item；

(2) for typically monitoring item, use wireless sensor network that the health status of monitoring item is monitored and record Health status monitoring amount；

Item is monitored for key, uses the mode that radio sensor network monitoring and personal monitoring combine to monitoring item Health status is monitored, if the wireless senser health status monitoring amount of certain crucial monitoring item is m₁, artificial health status is supervised It is measured as m₂, owing to may be affected by temperature during Sensor monitoring, introduce temperature correction factor ξ, for not by temperature shadow The sensor rung, makes ξ=1, for the sensor of temperature influence,Wherein T is that sensor is monitored Time ambient temperature, T₀For the standard temperature being suitable for during Sensor monitoring, then its final health status monitoring amount m uses following formula true Fixed:

$m=\mathrm{\ξ}\×\left\{\begin{array}{cc}{m}_1,& if\left|m_1-m_2\right|\≤c\\ m_2,& if\left|m_1-m_2\right|>c\end{array}\right.$

In formula, according to c, monitor the constant of item reasonable error range set；

Monitoring item is divided into general monitoring item and crucial monitoring item by this preferred embodiment, and carries out in different ways Monitoring, had both saved monitoring cost, had obtained again monitoring result with a high credibility.

Preferably, health status monitoring amount normalization is expressed as by described data preprocessing module 12:

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount reaches maximum:

$n=1-e^{-\frac{m-L}{H-L}}$

When being in the situation that correspondence monitoring item health status is best when health status monitoring amount minimizes,

$n=1-e^{-\frac{H-m}{H-L}}$

In formula, m represents the original health monitoring variable of a certain monitoring item, and n represents the health after this monitoring item normalization Status monitoring amount, L is the health status monitoring amount lower limit of this monitoring item, and H is the health status monitoring amount upper limit of this monitoring item Value.

The monitoring means taked due to different monitoring projects is different, and the order of magnitude of the monitoring result obtained is different, single Position is also different, and health status monitoring amount is normalized by this preferred embodiment, conveniently assembly is carried out comprehensive assessment.

Preferably, the described degree of risk determining assembly, including:

(1) by the health status monitoring amount weighted average after Monitoring Data normalization various for source, obtain assembly and be good for Health status monitoring index:

$s=\frac{{\Σ}_{i=1}^k{n}_i{w}_i}{{\Σ}_{i=1}^k{w}_i}$

In formula, s represents assembly health status monitoring index, n_iFor i-th monitoring item health status monitoring amount, i=1, 2 ..., k, w_iAccording to each health status monitoring amount n_iThe weight factor that significance level in assembly is arranged；

Set secure threshold T_s, T_s∈ [0.4,0.5], if health status monitoring index s is less than secure threshold T_s, then judge Health status monitoring index s is in exception；

(2) utilize history state of health data and historical failure rate data, set up the failure rate model repaired of equipment:

In formula, r is that equipment can repair fault rate, and s ' is equipment condition monitoring index, and a, b, d are three undetermined constants,For The corrected parameter produced according to actual application conditions difference；

Wherein, determined the value of parameter a, b, d by historical state data and historical failure rate data, particularly as follows:

If equipment inner assembly number is l, certain assembly z_jAt certain time period T_jThe number of times inside broken down is f_j, its correspondence Health status monitoring index is s_j, by the health status monitoring index s of multiple assemblies_jWith the number of times f broken down_jCollect, Health status monitoring index that then equipment is overall and fault rate computing formula can be repaired be represented by:

$s^{\′}=\frac{{\Σ}_{j=1}^l{s}_j}{l}$

$r=\frac{{\Σ}_{j=1}^l\frac{f_j}{T_j}}{l}$

By above-mentioned health status monitoring index with fault rate iteration can be repaired enter the failure rate model repaired of equipment, thus Determine the value of parameter a, b, d；

(3) according to the failure rate model repaired of equipment, degree of risk X of each assembly is tried to achieve_j:

$X_j=r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& \stackrel{\‾}{s_j},& \mathrm{...},& s_l\end{array}\right)-r\left(\begin{array}{ccccc}{s}_1,& \mathrm{...},& s_j,& \mathrm{...},& s_l\end{array}\right)$

In formula, j=1 ..., l,Represent and be in abnormal assembly z_jHealth status monitoring index, r (s₁..., s_j..., s_l) represent that each assemblies monitor index is s₁..., s_j..., s_lTime power system can repair fault rate.

This preferred embodiment sets up assembly health status monitoring index and the failure rate model repaired of power system, from And determining the degree of risk of each assembly, it is possible to the more component failure of weighing of science affects journey to what equipment dependability produced Degree, thus be conducive to preferentially keeping in repair for the faulty components that influence degree is big, save maintenance cost, maintenance policy is more than It is decided by the state of assembly itself, and is also dependent upon the component failures impact on equipment dependability, make maintenance policy more objective See and reliable.

Preferably, described combination maintainability and economic factors generate maintenance policy, including:

(1) predefine maintenance policy desired parameters by expert group and this parameter is stored in data base, described maintenance plan Slightly desired parameters includes: each assembly maintenance difficulty M when each monitoring item occurs abnormal_JiWith maintenance economic value E_Ji, the wind of assembly Danger degree X_j, described maintenance difficulty M_JiWith maintenance economic value E_JiShared weight w (X_j)、w(M_Ji)、w(E_Ji), wherein said dimension Repair economic value E_JThe ratio being worth with assembly for maintenance cost；

(2) set the health status monitoring index according to exception and determine that assembly to be repaired is as d_j, j=1 ..., l_d, l_dFor waiting to tie up Repair the number of assembly, according to assembly d to be repaired_jEach exception monitoring item i (i=1,2 ..., k) transfer corresponding maintenance difficulty M_Ji With maintenance economic value E_Ji, calculate comprehensive maintenance difficulty M of assembly to be repaired_Ji' with comprehensive maintenance economic value E_Ji':

${E_{Ji}}^{,}=\underset{i}{\overset{k}{\Σ}}{E}_{Ji}$

(3) the maintenance tendency degree of each assembly to be repaired is calculated

Maintenance tendency degree to each assembly to be repairedSort from big to small, so that it is determined that each assembly to be repaired Maintenance sequencing, i.e. preferential maintenance bigger maintenance tendency degreeCorresponding assembly to be repaired；It addition, according to assembly to be repaired Corresponding comprehensive maintenance difficulty M_Ji' determine corresponding maintenance program, thus generate the maintenance policy of optimum.

This preferred embodiment has formulated the generating mode of optimum maintenance policy, and method is objective simply, the generation of maintenance policy Consider the maintainability in addition to degree of risk and economic factors, add objectivity and reliability that maintenance policy is formulated, And in the face of in a large number wait the assembly to be repaired passed judgment on time, greatly reduce workload, improve work efficiency, and preferably protect Hold the concordance of judge.

In this application scenarios, set secure threshold T_s=0.5, it is relative that the exception of health status monitoring index passes judgment on precision Improve 8.5%, the reliability of equipment improves 8% relatively.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (3)

1. a sliding friction nano generator maintenance system, is characterized in that, including sliding friction nano generator and maintenance plan Slightly obtain system, described maintenance policy obtain system include data acquisition module, data preprocessing module, risk determine module, Maintenance policy generation module, described sliding friction nano generator, including:

Frictional layer；

The conducting element that described frictional layer lower contact is placed；

Conductive layer；

The upper surface of described frictional layer is staggered relatively with the lower surface of described conductive layer；

When externally applied forces make the upper surface of described frictional layer occur with the lower surface of described conductive layer relative sliding friction and When causing friction area to change, it is possible to export the signal of telecommunication by described conducting element and conductive layer to external circuit.

A kind of sliding friction nano generator maintenance system the most according to claim 1, is characterized in that, described frictional layer Friction electrode sequence difference is there is between the lower surface material of top surface and described conductive layer.

A kind of sliding friction nano generator maintenance system the most according to claim 2, is characterized in that, described frictional layer is Insulant or semi-conducting material.