CN111780809A - Rail vehicle part temperature and vibration monitoring and early warning method and system - Google Patents

Rail vehicle part temperature and vibration monitoring and early warning method and system Download PDF

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CN111780809A
CN111780809A CN202010704467.XA CN202010704467A CN111780809A CN 111780809 A CN111780809 A CN 111780809A CN 202010704467 A CN202010704467 A CN 202010704467A CN 111780809 A CN111780809 A CN 111780809A
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temperature
vibration
early warning
parameter
parameters
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CN111780809B (en
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黄文彬
刘阳
唐健
丁晓喜
邵毅敏
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Chongqing University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0421—Multiprocessor system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/22—Pc multi processor system
    • G05B2219/2214—Multicontrollers, multimicrocomputers, multiprocessing
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/25—Pc structure of the system
    • G05B2219/25033—Pc structure of the system structure, control, syncronization, data, alarm, connect I-O line to interface
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/25—Pc structure of the system
    • G05B2219/25187—Transmission of signals, medium, ultrasonic, radio
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/25—Pc structure of the system
    • G05B2219/25252—Microprocessor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2637—Vehicle, car, auto, wheelchair
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention provides a rail vehicle part temperature and vibration monitoring and early warning method, which comprises the following steps: s1, collecting temperature parameters and vibration parameters of parts of a railway vehicle; s2, preprocessing temperature parameters and vibration parameters of parts of the rail vehicle, and rejecting abnormal data in the temperature parameters and the vibration parameters; s3, early warning is carried out according to the temperature parameter and the vibration parameter after pretreatment; according to the invention, the temperature parameters and the vibration parameters of the rail vehicle parts can be acquired in real time, and abnormal data can be accurately eliminated, so that the early warning of the running state of the rail vehicle parts can be accurately carried out, corresponding faults can be accurately identified, and the running safety of the rail vehicle can be further ensured.

Description

Rail vehicle part temperature and vibration monitoring and early warning method and system
Technical Field
The invention relates to a monitoring method and a system thereof, in particular to a method and a system for monitoring and early warning the temperature and vibration of parts of a railway vehicle.
Background
Rail vehicles such as subways and light rails play an extremely important role in modern cities, effectively alleviate traffic jam in cities and improve the carrying capacity of commuters, so the rail vehicles are more and more favored by people, real-time monitoring and timely corresponding monitoring and early warning of parts of the rail vehicles are key for ensuring safe operation of the rail vehicles, temperature parameters and vibration parameters of the parts of the rail vehicles are two parameters which can most feed back the operation state of the rail vehicles, and how to ensure the effectiveness of the temperature parameters and the temperature parameters of the rail vehicles and the accuracy of the early warning is a technical problem all the time, and an effective means is not provided for solving the problem at present.
Disclosure of Invention
In view of the above, the present invention provides a rail vehicle component temperature and vibration monitoring and early warning method and a rail vehicle component temperature and vibration monitoring and early warning system, which can acquire temperature parameters and vibration parameters of rail vehicle components in real time and accurately eliminate abnormal data, thereby ensuring that rail vehicle component operation state early warning can be accurately performed, corresponding faults can be accurately identified, and further ensuring the safety of rail vehicle operation.
The invention provides a rail vehicle part temperature and vibration monitoring and early warning method, which comprises the following steps:
s1, collecting temperature parameters and vibration parameters of parts of a railway vehicle;
s2, preprocessing temperature parameters and vibration parameters of parts of the rail vehicle, and rejecting abnormal data in the temperature parameters and the vibration parameters;
and S3, early warning is carried out according to the temperature parameter and the vibration parameter after pretreatment.
Further, the preprocessing of the temperature parameters of the rail vehicle parts specifically comprises:
S2A1, judging whether the time difference delta T between two adjacent temperature parameter acquisition of the same temperature monitoring point is larger than a set time threshold value T or notthIf not, go to step S2a 2; if yes, go to step S2A 3;
S2A2, judging whether the temperature difference delta TEM1 between two adjacent temperature parameters of the same temperature monitoring point is greater than or notSet temperature difference threshold TEMthIf not, indicating that the temperature parameters acquired in the two adjacent times are normal, and if not, replacing the temperature parameter acquired in the two adjacent times with the temperature parameter acquired in the last time;
S2A3, comparing the latter temperature parameter in the two adjacent temperature parameters with the temperature parameter of the adjacent temperature acquisition point at the same time of the latter temperature parameter, and if the temperature difference delta TEM2 is larger than the temperature difference threshold TEMthIf the temperature parameter of the adjacent temperature parameters is abnormal, the temperature parameter of the adjacent temperature acquisition point at the same time as the latter temperature parameter is used for replacing one of the adjacent temperature parameters, and if the temperature parameter of the adjacent temperature acquisition point is not abnormal, the latter temperature parameter of the adjacent temperature parameters is normal.
Further, the preprocessing of the vibration parameters of the rail vehicle parts specifically comprises:
S2B1, sequencing the acquired vibration data according to the value, and constructing a vibration matrix A:
Figure BDA0002594192710000021
wherein n represents the number of the vibration sensors, and l represents the number of data collected by each sensor;
S2B2, constructing an energy matrix B and a median matrix C:
Figure BDA0002594192710000022
Figure BDA0002594192710000031
S2B3, constructing an upper threshold matrix D:
Figure BDA0002594192710000032
wherein ,
Figure BDA0002594192710000033
is the coverage factor;
S2B4, constructing a lower threshold matrix E:
Figure BDA0002594192710000034
s2b5. constructing encoding matrices Fun1(D) and Fun1 (E):
for the encoding matrix Fun1 (D): setting elements larger than 0 in the upper threshold matrix D as 1, and setting elements smaller than 0 as 0;
for the encoding matrix Fun1 (E): setting elements larger than 0 in the lower threshold matrix E as 1, and setting elements smaller than 0 as 0;
S2B6, constructing an encoding matrix M:
setting elements with the corresponding positions of 1 in the encoding matrix Fun1(D) and the matrix Fun1(E) as 1, and setting the rest as 0;
S2B7, operating the vibration matrix and the coding matrix M to obtain a processed vibration matrix F:
F＝A×M。
further, in step S3, a vibration parameter warning is performed by the following method:
determining an early warning threshold value p':
Figure BDA0002594192710000035
determination of the alarm threshold w':
Figure BDA0002594192710000041
wherein p is a preset initial early warning threshold, w is a preset initial warning threshold, and K0Is the average slope of the vibration parameter, KtThe slope corresponding to the current vibration parameter; wherein,
Figure BDA0002594192710000042
wherein l' is the number of the preprocessed vibration data, k represents the number of segments into which a curve composed of the preprocessed vibration data is divided, vi+l'/kRepresenting the amplitude, v, of the preprocessed vibration data after segmentationiTo prepareThe amplitude value of the ith data point in the processed vibration data, delta 1 is the time interval between two adjacent data points after the segmentation of the preprocessed data, Kt＝(vt-vt-1) A/Δ 2, wherein vtIs the value of the t-th data point, v, of the jth sensor in the vibration matrix At-1The value of the t-1 data point of the jth sensor in the vibration matrix A; Δ 2 is the data point v for the jth sensort-1And data point vtThe time interval in between;
when the filtered vibration parameter is larger than an early warning threshold value p', early warning is carried out;
and when the filtered vibration parameter is larger than an alarm threshold value w', alarming.
Further, in step S3, a temperature parameter warning is performed by the following method:
determining a temperature early warning threshold T:
T＝S*(Te+Ts) (ii) a Wherein S is a temperature coefficient factor T of the rail train in different running stateseIs an ambient temperature value, TsA pre-warning threshold value for the set initial temperature;
and comparing the temperature early warning threshold with the preprocessed temperature parameter, and giving an alarm when the preprocessed temperature parameter is greater than or equal to the temperature early warning threshold T.
Correspondingly, the invention also provides a rail vehicle part temperature and vibration monitoring and early warning system, which comprises a temperature sensor unit, a vibration sensor unit, a relay processing module, a sensor node module and a monitoring server;
the temperature sensor unit comprises a plurality of temperature sensors and is respectively arranged at each set monitoring point, and the vibration sensor units are a plurality of vibration sensor units and are respectively arranged at each set monitoring point;
the temperature sensor unit and the vibration and vibration sensor unit are connected with the sensor node module, the sensor node module is in communication connection with the relay processing module, and the relay processing module is in communication connection with the monitoring server.
Further, the sensor node module comprises a microprocessor, a memory and a wireless communication module;
the temperature sensor unit and the vibration sensor unit are in communication connection with a microprocessor through a wireless communication module, the microprocessor is in communication connection with a memory, and the microprocessor is in communication connection with a relay processing module.
Further, the relay processing module comprises a relay processor, a gateway and a mobile communication module;
the relay processor is in communication connection with the monitoring server through the mobile communication module, and the relay processor is in communication connection with the microprocessor through the gateway.
Further, the wireless communication module is a ZigBee module, a Bluetooth module or a UWB module.
The invention has the beneficial effects that: according to the invention, the temperature parameters and the vibration parameters of the rail vehicle parts can be acquired in real time, and abnormal data can be accurately eliminated, so that the early warning of the running state of the rail vehicle parts can be accurately carried out, corresponding faults can be accurately identified, and the running safety of the rail vehicle can be further ensured.
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The invention is further described below with reference to the following figures and examples:
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of the system of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings of the specification:
the invention provides a rail vehicle part temperature and vibration monitoring and early warning method, which comprises the following steps:
s1, collecting temperature parameters and vibration parameters of parts of a railway vehicle;
s2, preprocessing temperature parameters and vibration parameters of parts of the rail vehicle, and rejecting abnormal data in the temperature parameters and the vibration parameters;
s3, early warning is carried out according to the temperature parameter and the vibration parameter after pretreatment; according to the invention, the temperature parameters and the vibration parameters of the rail vehicle parts can be acquired in real time, and abnormal data can be accurately eliminated, so that the early warning of the running state of the rail vehicle parts can be accurately carried out, corresponding faults can be accurately identified, and the running safety of the rail vehicle can be further ensured.
In this embodiment, the preprocessing of the temperature parameters of the rail vehicle components specifically includes:
S2A1, judging whether the time difference delta T between two adjacent temperature parameter acquisition of the same temperature monitoring point is larger than a set time threshold value T or notthIf not, go to step S2a 2; if yes, go to step S2A 3;
S2A2, judging whether the temperature difference delta TEM1 between two adjacent temperature parameters collected at the same temperature monitoring point is larger than a set temperature difference threshold TEMthIf not, indicating that the temperature parameters acquired in the two adjacent times are normal, and if not, replacing the temperature parameter acquired in the two adjacent times with the temperature parameter acquired in the last time;
S2A3, comparing the latter temperature parameter in the two adjacent temperature parameters with the temperature parameter of the adjacent temperature acquisition point at the same time of the latter temperature parameter, and if the temperature difference delta TEM2 is larger than the temperature difference threshold TEMthIf the temperature parameter of the last temperature parameter in the two adjacent temperature parameters is abnormal, replacing one temperature parameter in the two adjacent temperature parameters with the temperature parameter of the adjacent temperature acquisition point at the same time as the last temperature parameter, and if the temperature parameter of the last temperature acquisition point is not normal, indicating that the last temperature parameter in the two adjacent temperature parameters is normal; the pretreatment of the temperature parameter is described below with a specific example:
assuming there are A, B, C three temperature monitoring points that monitor the same component, such as three bearings on the same shaft, for temperature monitoring point a: acquiring temperature data once at the time of t1, acquiring temperature data once at the time of t2, acquiring temperature data once at the time of t3, wherein A and B are adjacent temperature acquisition points, and B and C, B and A are both adjacent temperature acquisition points; the two temperature parameters acquired at the time t1 and the time t2 are two adjacent temperature parameters, and the two temperature parameters acquired at the time t2 and the time t3 are two adjacent temperature parameters:
(1) judging whether the time difference between the time T1 and the time T2 is larger than a set time threshold value T or notthIf not, the difference value of the two temperatures at the detection point A at the time t1 and the time t2 is compared with the set temperature difference threshold TEMthAnd comparing, if the difference value between the two temperatures at the time point t1 and the time point a at the time point t2 is smaller than a temperature difference threshold value, indicating that the temperature value at the time point t2 is normal, and if the difference value between the two temperatures at the time point t1 and the time point a at the time point t2 is larger than the temperature difference threshold value, indicating that temperature jump exists at the time point t2, taking the temperature value acquired at the time point t1 as the temperature value at the time point t2, and acquiring the temperature value at the current time point t2 of the community.
(2) If the time difference between the time T1 and the time T2 is larger than the set time threshold TthIf the temperature difference value is smaller than the temperature difference threshold TEM, the temperature difference value is judged to be the temperature difference threshold TEM, and if the temperature difference value is smaller than the temperature difference threshold TEM, the condition that the temperature detection equipment is disconnected is indicated, the temperature collected by the temperature collection point B at the time t2 is compared with the temperature collected by the temperature collection point A at the time t2 at the timethThen, the temperature data still marked as the temperature acquisition point at time a at t2 is normal, if the two temperature differences are greater than the temperature difference threshold TEMthThen the temperature value at point B at time t2 is recorded as the temperature value at point a at time t 2.
Then for point B, its neighboring temperature acquisition points have points a and C, then in content (2), the temperature of point B at time t2 is compared with the temperature of point a at time t2 and the temperature of point C at time t2, respectively, to obtain a difference, and if both are greater than the temperature difference threshold TEMthThen, the difference AB between the point B and the point A at the time t2 is compared with the difference CB between the point B and the point C at the time t2, if AB > CB, the temperature value at the time t2 is recorded as the temperature value at the time t2 of the point B, and the temperature value at the time B at the time t2 is recorded. By the method, abnormal data in the temperature data can be accurately removed, the continuity of the data is ensured, and the accuracy of final early warning is further ensured.
In this embodiment, the preprocessing of the vibration parameters of the rail vehicle parts specifically includes:
S2B1, sequencing the acquired vibration data according to the value, and constructing a vibration matrix A:
Figure BDA0002594192710000081
wherein n represents the number of the vibration sensors, and l represents the number of data collected by each sensor;
S2B2, constructing an energy matrix B and a median matrix C:
Figure BDA0002594192710000082
Figure BDA0002594192710000083
S2B3, constructing an upper threshold matrix D:
Figure BDA0002594192710000084
wherein ,
Figure BDA0002594192710000085
is the coverage factor;
S2B4, constructing a lower threshold matrix E:
Figure BDA0002594192710000086
s2b5. constructing encoding matrices Fun1(D) and Fun1 (E):
for the encoding matrix Fun1 (D): setting elements larger than 0 in the upper threshold matrix D as 1, and setting elements smaller than 0 as 0;
for the encoding matrix Fun1 (E): setting elements larger than 0 in the lower threshold matrix E as 1, and setting elements smaller than 0 as 0;
S2B6, constructing an encoding matrix M:
setting elements with the corresponding positions of 1 in the encoding matrix Fun1(D) and the matrix Fun1(E) as 1, and setting the rest as 0;
S2B7, operating the vibration matrix and the coding matrix M to obtain a processed vibration matrix F:
f ═ a × M. By the method, abnormal data in the vibration data can be accurately removed, and the accuracy of a final early warning result is ensured.
In this embodiment, in step S3, the vibration parameter is pre-warned by the following method:
determining an early warning threshold value p':
Figure BDA0002594192710000091
determination of the alarm threshold w':
Figure BDA0002594192710000092
wherein p is a preset initial early warning threshold, w is a preset initial warning threshold, and K0Is the average slope, K, of the jth sensor and the processed vibration parametertThe slope corresponding to the current vibration parameter; wherein,
Figure BDA0002594192710000093
wherein l' is the number of the preprocessed vibration data, k represents the number of segments into which a curve composed of the preprocessed vibration data is divided, vi+l'/kRepresenting the amplitude, v, of the preprocessed vibration data after segmentationiIs the amplitude of the ith data point in the preprocessed vibration data, Delta 1 is the time interval between two adjacent data points after the preprocessed data are segmented, Kt＝(vt-vt-1) A/Δ 2, wherein vtIs the value of the t-th data point, v, of the jth sensor in the vibration matrix At-1The value of the t-1 data point of the jth sensor in the vibration matrix A; Δ 2 is the data point v for the jth sensort-1And data point vtThe time interval in between;
when the filtered vibration parameter is larger than an early warning threshold value p', early warning is carried out; after early warning, the train can continue to run in the current state, stops running after running to a formulated station, and then carries out corresponding maintenance;
and when the filtered vibration parameter is larger than an alarm threshold value w', alarming, and when alarm information appears, indicating that the train must stop running immediately and be overhauled immediately.
In this embodiment, in step S3, the temperature parameter is pre-warned by the following method:
determining a temperature early warning threshold T:
T＝S*(Te+Ts) (ii) a Wherein S is a temperature coefficient factor T of the rail train in different running stateseIs an ambient temperature value, TsA pre-warning threshold value for the set initial temperature;
and comparing the temperature early warning threshold with the preprocessed temperature parameter, and giving an alarm when the preprocessed temperature parameter is greater than or equal to the temperature early warning threshold T.
Correspondingly, the invention also provides a rail vehicle part temperature and vibration monitoring and early warning system, which comprises a temperature sensor unit, a vibration sensor unit, a relay processing module, a sensor node module and a monitoring server;
the temperature sensor unit comprises a plurality of temperature sensors and is respectively arranged at each set monitoring point, and the vibration sensor units are a plurality of vibration sensor units and are respectively arranged at each set monitoring point;
the temperature sensor unit and the vibration and vibration sensor unit are connected with the sensor node module, the sensor node module is in communication connection with the relay processing module, and the relay processing module is in communication connection with the monitoring server.
Specifically, the method comprises the following steps: the sensor node module comprises a microprocessor, a memory and a wireless communication module;
the temperature sensor unit and the vibration sensor unit are in communication connection with the microprocessor through the wireless communication module, the microprocessor is in communication connection with the memory, the microprocessor is in communication connection with the relay processing module, and the microprocessor is used for packaging parameters output by the sensors and sending the parameters to the relay processor through the gateway.
The relay processing module comprises a relay processor, a gateway and a mobile communication module;
the relay processor is in communication connection with the monitoring server through the mobile communication module, and the relay processor is in communication connection with the microprocessor through the gateway. In actual arrangement, the sensors of the temperature sensor unit and the vibration sensor unit are not arranged together, that is, at the same monitoring point, both the temperature sensor and the vibration sensor are arranged, so that a plurality of temperature sensors, a plurality of vibration sensors and a sensor node module can form a sensor node according to actual working conditions, and therefore, the whole rail train is provided with a plurality of sensor nodes; the relay processor further packages the parameters sent by each sensor node and sends the parameters to the monitoring server, wherein the monitoring server processes the parameters according to the method and gives an alarm through an alarm connected with the monitoring server, and the alarm can be an audible and visual alarm or a display screen or the display screen and the audible and visual alarm act simultaneously.
The wireless communication module is a ZigBee module, a Bluetooth module or a UWB module; wherein, the microprocessor and the relay processor adopt STM32F405RGT6 and peripheral circuits thereof.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (9)

1. A rail vehicle part temperature and vibration monitoring and early warning method is characterized by comprising the following steps: the method comprises the following steps:
s1, collecting temperature parameters and vibration parameters of parts of a railway vehicle;
s2, preprocessing temperature parameters and vibration parameters of parts of the rail vehicle, and rejecting abnormal data in the temperature parameters and the vibration parameters;
and S3, early warning is carried out according to the temperature parameter and the vibration parameter after pretreatment.
2. The rail vehicle part temperature and vibration monitoring and early warning method as claimed in claim 1, wherein: the pretreatment of the temperature parameters of the rail vehicle parts specifically comprises the following steps:
S2A1, judging whether the time difference delta T between two adjacent temperature parameter acquisition of the same temperature monitoring point is larger than a set time threshold value T or notthIf not, go to step S2a 2; if yes, go to step S2A 3;
S2A2, judging whether the temperature difference delta TEM1 between two adjacent temperature parameters collected at the same temperature monitoring point is larger than a set temperature difference threshold TEMthIf not, indicating that the temperature parameters acquired in the two adjacent times are normal, and if not, replacing the temperature parameter acquired in the two adjacent times with the temperature parameter acquired in the last time;
S2A3, comparing the latter temperature parameter in the two adjacent temperature parameters with the temperature parameter of the adjacent temperature acquisition point at the same time of the latter temperature parameter, and if the temperature difference delta TEM2 is larger than the temperature difference threshold TEMthIf the temperature parameter of the adjacent temperature parameters is abnormal, the temperature parameter of the adjacent temperature acquisition point at the same time as the latter temperature parameter is used for replacing one of the adjacent temperature parameters, and if the temperature parameter of the adjacent temperature acquisition point is not abnormal, the latter temperature parameter of the adjacent temperature parameters is normal.
3. The rail vehicle part temperature and vibration monitoring and early warning method as claimed in claim 1, wherein: the preprocessing of the vibration parameters of the rail vehicle parts specifically comprises the following steps:
S2B1, sequencing the acquired vibration data according to the value, and constructing a vibration matrix A:
Figure FDA0002594192700000011
wherein n represents the number of the vibration sensors, and l represents the number of data collected by each sensor;
S2B2, constructing an energy matrix B and a median matrix C:
Figure FDA0002594192700000021
Figure FDA0002594192700000022
S2B3, constructing an upper threshold matrix D:
Figure FDA0002594192700000023
wherein ,
Figure FDA0002594192700000024
is the coverage factor;
S2B4, constructing a lower threshold matrix E:
Figure FDA0002594192700000025
s2b5. constructing encoding matrices Fun1(D) and Fun1 (E):
for the encoding matrix Fun1 (D): setting elements larger than 0 in the upper threshold matrix D as 1, and setting elements smaller than 0 as 0;
for the encoding matrix Fun1 (E): setting elements larger than 0 in the lower threshold matrix E as 1, and setting elements smaller than 0 as 0;
S2B6, constructing an encoding matrix M:
setting elements with the corresponding positions of 1 in the encoding matrix Fun1(D) and the matrix Fun1(E) as 1, and setting the rest as 0;
S2B7, operating the vibration matrix and the coding matrix M to obtain a processed vibration matrix F:
F＝A×M。
4. the rail vehicle part temperature and vibration monitoring and early warning method as claimed in claim 3, wherein: in step S3, vibration parameter warning is performed by the following method:
determining an early warning threshold value p':
Figure FDA0002594192700000031
determination of the alarm threshold w':
Figure FDA0002594192700000032
wherein p is a preset initial early warning threshold, w is a preset initial warning threshold, and K0Is the average slope of the vibration parameter, KtThe slope corresponding to the current vibration parameter; wherein,
Figure FDA0002594192700000033
wherein l' is the number of the preprocessed vibration data, k represents the number of segments into which a curve composed of the preprocessed vibration data is divided, vi+l'/kRepresenting the amplitude, v, of the preprocessed vibration data after segmentationiIs the amplitude of the ith data point in the preprocessed vibration data, Delta 1 is the time interval between two adjacent data points after the preprocessed data are segmented, Kt＝(vt-vt-1) A/Δ 2, wherein vtIs the value of the t-th data point, v, of the jth sensor in the vibration matrix At-1The value of the t-1 data point of the jth sensor in the vibration matrix A; Δ 2 is the data point v for the jth sensort-1And data point vtThe time interval in between;
when the filtered vibration parameter is larger than an early warning threshold value p', early warning is carried out;
and when the filtered vibration parameter is larger than an alarm threshold value w', alarming.
5. The rail vehicle part temperature and vibration monitoring and early warning method as claimed in claim 2, wherein: in step S3, temperature parameter warning is performed by the following method:
determining a temperature early warning threshold T:
T＝S*(Te+Ts) (ii) a Wherein S is a temperature coefficient factor T of the rail train in different running stateseIs an ambient temperature value, TsA pre-warning threshold value for the set initial temperature;
and comparing the temperature early warning threshold with the preprocessed temperature parameter, and giving an alarm when the preprocessed temperature parameter is greater than or equal to the temperature early warning threshold T.
6. The utility model provides a rail vehicle spare part temperature and vibration monitoring early warning system which characterized in that: the system comprises a temperature sensor unit, a vibration sensor unit, a relay processing module, a sensor node module and a monitoring server;
the temperature sensor unit comprises a plurality of temperature sensors and is respectively arranged at each set monitoring point, and the vibration sensor units are a plurality of vibration sensor units and are respectively arranged at each set monitoring point;
the temperature sensor unit and the vibration and vibration sensor unit are connected with the sensor node module, the sensor node module is in communication connection with the relay processing module, and the relay processing module is in communication connection with the monitoring server.
7. The rail vehicle component temperature and vibration monitoring and early warning system of claim 6, wherein: the sensor node module comprises a microprocessor, a memory and a wireless communication module;
the temperature sensor unit and the vibration sensor unit are in communication connection with a microprocessor through a wireless communication module, the microprocessor is in communication connection with a memory, and the microprocessor is in communication connection with a relay processing module.
8. The rail vehicle component temperature and vibration monitoring and early warning system of claim 7, wherein: the relay processing module comprises a relay processor, a gateway and a mobile communication module;
the relay processor is in communication connection with the monitoring server through the mobile communication module, and the relay processor is in communication connection with the microprocessor through the gateway.
9. The rail vehicle component temperature and vibration monitoring and early warning system of claim 7, wherein: the wireless communication module is a ZigBee module, a Bluetooth module or a UWB module.
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