CN109325310B - High-speed train intermittent fault detection method based on multiple T-square control diagram - Google Patents

High-speed train intermittent fault detection method based on multiple T-square control diagram Download PDF

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CN109325310B
CN109325310B CN201811250111.2A CN201811250111A CN109325310B CN 109325310 B CN109325310 B CN 109325310B CN 201811250111 A CN201811250111 A CN 201811250111A CN 109325310 B CN109325310 B CN 109325310B
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周东华
赵英弘
何潇
卢晓
钟麦英
王友清
王建东
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Shandong University of Science and Technology
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Abstract

The invention discloses a high-speed train intermittent fault detection method based on a multiple T-square control diagram, belonging to the field of fault diagnosis; the method can detect intermittent faults with smaller amplitude and limited duration, deduce the occurrence time and disappearance time of the intermittent faults, and remarkably improve the detection effect of the data driving method on the intermittent faults; the method comprises the following steps: training data is collected and preprocessed, intermittent fault parameters of a key system of the high-speed train are analyzed, on-line fault detection and false alarm elimination are carried out, and time window selection and time of occurrence and disappearance of the intermittent fault are deduced in real time. The invention effectively guarantees the actual application requirements of the intermittent fault detection of the key system of the high-speed train.

Description

High-speed train intermittent fault detection method based on multiple T-square control diagram
Technical Field
The invention belongs to the field of fault diagnosis, and particularly relates to a high-speed train intermittent fault detection method based on a multiple T-square control diagram.
Background
In recent years, fault detection of a critical system of a high-speed train has become a subject of hot research. However, for many years, people mostly focus on the problem of detecting continuous faults only, and the problem of detecting intermittent faults is less studied. On the other hand, with the rapid development of electronic, information and other technologies, a special type of fault, i.e., intermittent fault, different from the conventional continuous fault form, is becoming more important. The high-speed train brake control system operates in a complex environment and is easy to generate intermittent faults. On the one hand, the electronic brake control unit is composed of a complex electronic circuit, and the intermittent fault of the controller is caused by loosening of the control circuit due to cold joint aging and the like. In addition, the running environment of the high-speed train is complex, and the vehicle-mounted sensor is easily affected by vibration, electromagnetic interference and the like, so that the sensor is in intermittent failure.
Intermittent faults are faults of a type that have a finite duration and that can automatically disappear without external compensation measures, allowing the system to resume acceptable performance. Compared with continuous faults, intermittent faults occur randomly and repeatedly, and have obvious cumulative effects, namely, the occurrence frequency and the duration of the faults gradually increase with the passage of time, and the intermittent faults finally evolve into continuous faults. Because the occurrence and disappearance of intermittent faults have randomness, the diagnosis standard of the intermittent faults requires that the occurrence and disappearance time of the faults are detected simultaneously, the diagnosis performance meets the requirements that the occurrence time of the faults is determined before the current fault disappears, and the disappearance time of the faults is determined before the next fault occurs. The detection result is more beneficial to analyzing the running state of the system, making reasonable maintenance and repair strategies and designing fault-tolerant control laws aiming at intermittent faults.
The characteristics of small intermittent fault amplitude and limited duration make it difficult for the conventional data-driven fault detection method to be directly applied to intermittent fault detection
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a high-speed train intermittent fault detection method based on a multiple T-square control diagram, which is reasonable in design, overcomes the defects in the prior art and has good effect.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the intermittent fault detection method of the high-speed train based on the multiple T-square control diagram comprises the following steps:
step 1: offline training; the method specifically comprises the following steps:
step 1.1: constructing a normal working condition measurement matrix:
[X 1 ,X 2 ,…,X k ,…,X N ]∈R m×N ；
wherein X is k ∈R m×1 Is a column vector; m is the detectedThe number of sensors included in the system; n is the number of independent samples included in each sensor;
step 1.2: calculating sample mean of training data
Figure BDA0001841476630000021
And covariance matrix S, i.e
Figure BDA0001841476630000022
Step 1.3: according to experience or by analyzing historical fault data, the intermittent fault direction xi of the key system of the high-speed train is given q Lower boundary of fault amplitude
Figure BDA0001841476630000023
Fault duration lower bound->
Figure BDA0001841476630000024
And failure vanishing time lower bound->
Figure BDA0001841476630000025
And calculates the following formula:
Figure BDA0001841476630000026
Figure BDA0001841476630000027
wherein W is * And W is # Two time windows;
Figure BDA0001841476630000028
W＝1,2,…,W # ；/>
Figure BDA0001841476630000029
is an upper bound for theoretically detecting delays to faults; />
Figure BDA00018414766300000210
Is the upper bound of the detection delay for the disappearance of a fault in theory;
step 1.4: time window w=1, 2, …, W according to a given confidence level α # Respectively calculate
Figure BDA00018414766300000211
I.e.
Figure BDA00018414766300000212
Wherein F is a (p, N-p) is the upper score when the F distribution confidence level of the degree of freedom is p and the N-p is alpha, and is recorded
Figure BDA00018414766300000213
Step 2: detecting on-line faults; the method specifically comprises the following steps:
step 2.1: on-line collection of new measurement samples
Figure BDA00018414766300000221
And time window w=1, 2, …, W # Calculating statistics of each T-square control diagram in real time>
Figure BDA00018414766300000214
I.e.
Figure BDA00018414766300000215
Step 2.2: time window w=1, 2, …, W # The alarm time of each T-party control chart is updated in real time: alarm for fault occurrence
Figure BDA00018414766300000216
And failure vanishing alarm->
Figure BDA00018414766300000217
I.e.
Figure BDA00018414766300000218
Figure BDA00018414766300000219
Step 2.3: the method for eliminating false alarms on line specifically comprises the following steps:
step 2.3.1: if for W=W # The following is not true
Figure BDA00018414766300000220
Restarting step 2; otherwise, executing the step 2.4;
step 2.3.2: if W epsilon [ W ] exists * ,W # ]If the formula (1) is not established, restarting the step (2); otherwise, executing the step 2.4;
step 2.3.3: if W exists, W' E [ W ] * ,W # ]So that the following is true
Figure BDA0001841476630000031
Wherein the method comprises the steps of
Figure BDA0001841476630000032
If the set is empty, restarting the step 2, otherwise executing the step 2.4;
step 2.4: the time window is selected, and the method specifically comprises the following steps:
step 2.4.1: sequentially checking w= { W * …,1}, if the formula (1) is not established, setting W o =w+1; otherwise wo=1;
step 2.4.2: sequentially checking w= { W * …, wo if W' ∈ (W, W) # ]So that (2) is established, then wo=w+1 is reset;
step 2.4.3: for W= { W * ,…,W o ' MeterCalculation of
Figure BDA0001841476630000033
Figure BDA0001841476630000034
Step 2.4.4: sequentially checking w= { W * ,…,W o If W' ∈ (W, W) # ]So that the following formula is established, reset W o ＝W+1；
Figure BDA0001841476630000035
Step 2.5: the intermittent fault occurrence and disappearance time deducing specifically comprises the following steps:
step 2.5.1: final inference for calculating intermittent fault occurrence and disappearance time
Figure BDA0001841476630000036
And->
Figure BDA0001841476630000037
I.e.
Figure BDA0001841476630000038
Step 2.5.2: deducing that the intermittent fault is in
Figure BDA0001841476630000039
Occurs within the moment>
Figure BDA00018414766300000310
And vanishes in time.
The invention has the beneficial technical effects that:
the invention provides a new method for detecting intermittent faults, which provides a selection criterion of multiple time windows, can detect intermittent faults with smaller amplitude and limited duration, deduces the occurrence time and disappearance time of the intermittent faults, and obviously improves the detection effect of the data driving method on the intermittent faults.
Drawings
Fig. 1 is a schematic diagram of simulation results of intermittent fault recurrence and disappearance.
Fig. 2 is a schematic diagram of an original T-party control detection result when false alarm elimination is not performed.
Fig. 3 is a schematic diagram of intermittent fault detection results after false alarm elimination and time window selection.
Fig. 4 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
1. the intermittent fault detection method of the high-speed train based on the multiple T-party control diagram has a flow shown in fig. 4 and comprises the following steps:
step 1: offline training; the method specifically comprises the following steps:
step 1.1: constructing a normal working condition measurement matrix:
[X 1 ,X 2 ,…,X k ,…,X N ]∈R m×N ；
wherein X is k ∈R m×1 Is a column vector; m is the number of sensors contained in the detected system; n is the number of independent samples included in each sensor;
step 1.2: calculating sample mean of training data
Figure BDA0001841476630000041
And covariance matrix S, i.e.)>
Figure BDA0001841476630000042
Step 1.3: according to experience or by analyzing historical fault data, the intermittent fault direction xi of the key system of the high-speed train is given q Lower boundary of fault amplitude
Figure BDA0001841476630000043
Fault duration lower bound->
Figure BDA0001841476630000044
And failure vanishing time lower bound->
Figure BDA0001841476630000045
And calculates the following formula:
Figure BDA0001841476630000046
Figure BDA0001841476630000047
wherein W is * And W is # Two time windows;
Figure BDA0001841476630000048
W＝1,2,…,W # ；/>
Figure BDA0001841476630000049
is an upper bound for theoretically detecting delays to faults; />
Figure BDA00018414766300000410
Is the upper bound of the detection delay for the disappearance of a fault in theory;
step 1.4: time window w=1, 2, …, W according to a given confidence level α # Respectively calculate
Figure BDA00018414766300000411
I.e.
Figure BDA00018414766300000412
Wherein F is a (p, N-p) is the degree of freedom pCounting the upper score when the F distribution confidence level of N-p is alpha
Figure BDA00018414766300000413
Step 2: detecting on-line faults; the method specifically comprises the following steps:
step 2.1: on-line collection of new measurement samples
Figure BDA00018414766300000414
And time window w=1, 2, …, W # Calculating statistics of each T-square control diagram in real time>
Figure BDA00018414766300000415
I.e.
Figure BDA00018414766300000416
Step 2.2: time window w=1, 2, …, W # The alarm time of each T-party control chart is updated in real time: alarm for fault occurrence
Figure BDA0001841476630000051
And failure vanishing alarm->
Figure BDA0001841476630000052
I.e.
Figure BDA0001841476630000053
Figure BDA0001841476630000054
Step 2.3: the method for eliminating false alarms on line specifically comprises the following steps:
step 2.3.1: if for W=W # The following is not true
Figure BDA0001841476630000055
Restarting step 2; otherwise, executing the step 2.4;
step 2.3.2: if W epsilon [ W ] exists * ,W # ]If the formula (1) is not established, restarting the step (2); otherwise, executing the step 2.4;
step 2.3.3: if W exists, W' E [ W ] * ,W # ]So that the following is true
Figure BDA0001841476630000056
Wherein the method comprises the steps of
Figure BDA0001841476630000057
If the set is empty, restarting the step 2, otherwise executing the step 2.4; />
Step 2.4: the time window is selected, and the method specifically comprises the following steps:
step 2.4.1: sequentially checking w= { W * …,1}, if the formula (1) is not established, setting W o =w+1; otherwise W o ＝1；
Step 2.4.2: sequentially checking w= { W * ,…,W o If W' ∈ (W, W) # ]So that the expression (2) is established, reset W o ＝W+1；
Step 2.4.3: for W= { W * ,…,W o Calculation of
Figure BDA0001841476630000058
Figure BDA0001841476630000059
Step 2.4.4: sequentially checking w= { W * …, wo if W' ∈ (W, W) # ]So that the following formula holds, then wo=w+1 is reset;
Figure BDA00018414766300000510
step 2.5: the intermittent fault occurrence and disappearance time deducing specifically comprises the following steps:
step 2.5.1: final inference for calculating intermittent fault occurrence and disappearance time
Figure BDA00018414766300000511
And->
Figure BDA00018414766300000512
I.e.
Figure BDA00018414766300000513
Step 2.5.2: deducing that the intermittent fault is in
Figure BDA00018414766300000514
Occurs within the moment>
Figure BDA00018414766300000515
And vanishes in time.
2. Simulation study
The simulation model is selected as follows:
Figure BDA0001841476630000061
firstly, 5000 training data are generated according to the model, and the training data represent observation data under normal working conditions; then generating 500 test data according to the model, and adding direction xi from 201 st data q ＝[0.2425,0.9701] T Intermittent failure of (a); intermittent fault amplitude lower bound is
Figure BDA0001841476630000062
The duration of intermittent faults and the lower limit of the disappearance time are +.>
Figure BDA0001841476630000063
The simulation results are shown in fig. 1,2 and 3.
The left ordinate in fig. 1 shows the true value of the 1/4 failure amplitude, and the solid line in the figure represents the recurrence and disappearance of intermittent failure. The ordinate on the right side of fig. 2 represents the detection result of the 1/10 window length, and the dashed line in the figure represents the original T-square control detection result when false alarm elimination is not performed. The broken line in fig. 3 shows the intermittent fault detection result after false alarm elimination and time window selection, if the corresponding time window has no broken line when the fault occurs, it represents that we do not select the time window when predicting the occurrence and disappearance time of the fault in real time.
Table 1 gives our results of the inference of time to occurrence and disappearance of each intermittent fault, where μ q Representing the actual occurrence time of intermittent faults, the two following two columns represent the deduction of our method on the occurrence time, namely in the algorithm step
Figure BDA0001841476630000067
Similarly, v q The real vanishing time representing intermittent faults, the two latter columns are the deduction of our method on the vanishing time, namely +.>
Figure BDA0001841476630000068
TABLE 1
Figure BDA0001841476630000066
It is easy to see that the method can effectively reduce false alarm and infer the occurrence and disappearance time of intermittent faults, and the method can detect intermittent faults with smaller amplitude and limited duration, thereby remarkably improving the detection effect of the data driving method on the intermittent faults.
It should be understood that the above description is not intended to limit the invention to the particular embodiments disclosed, but to limit the invention to the particular embodiments disclosed, and that the invention is not limited to the particular embodiments disclosed, but is intended to cover modifications, adaptations, additions and alternatives falling within the spirit and scope of the invention.

Claims (1)

1. A high-speed train intermittent fault detection method based on a multiple T-party control diagram is characterized in that: the method comprises the following steps:
step 1: offline training; the method specifically comprises the following steps:
step 1.1: constructing a normal working condition measurement matrix:
[X 1 ,X 2 ,…,X k ,…,X N ]∈R m×N ；
wherein X is k ∈R m×1 Is a column vector; m is the number of sensors contained in the detected system; n is the number of independent samples included in each sensor;
step 1.2: calculating sample mean of training data
Figure FDA0001841476620000011
And covariance matrix S, i.e
Figure FDA0001841476620000012
Step 1.3: according to experience or by analyzing historical fault data, the intermittent fault direction xi of the key system of the high-speed train is given q Lower boundary of fault amplitude
Figure FDA0001841476620000013
Fault duration lower bound->
Figure FDA0001841476620000014
And failure vanishing time lower bound->
Figure FDA0001841476620000015
And calculates the following formula:
Figure FDA0001841476620000016
Figure FDA0001841476620000017
wherein W is * And W is # Two time windows;
Figure FDA00018414766200000117
W＝1,2,…,W # ；/>
Figure FDA0001841476620000018
is an upper bound for theoretically detecting delays to faults; />
Figure FDA0001841476620000019
Is the upper bound of the detection delay for the disappearance of a fault in theory;
step 1.4: time window w=1, 2, …, W according to a given confidence level α # Respectively calculate
Figure FDA00018414766200000110
I.e.
Figure FDA00018414766200000111
Wherein F is a (p, N-p) is the upper score when the F distribution confidence level of the degree of freedom is p and N-p is alpha, and delta is recorded 2 ＝δ 1 2 ；
Step 2: detecting on-line faults; the method specifically comprises the following steps:
step 2.1: on-line collection of new measurement samples
Figure FDA00018414766200000112
And time window w=1, 2, …, W # Calculating statistics of each T-square control diagram in real time>
Figure FDA00018414766200000113
I.e.
Figure FDA00018414766200000114
Step 2.2: time window w=1, 2, …, W # The alarm time of each T-party control chart is updated in real time: alarm for fault occurrence
Figure FDA00018414766200000115
And failure vanishing alarm->
Figure FDA00018414766200000116
I.e.
Figure FDA0001841476620000021
Figure FDA0001841476620000022
Step 2.3: the method for eliminating false alarms on line specifically comprises the following steps:
step 2.3.1: if for W=W # The following is not true
Figure FDA0001841476620000023
Restarting step 2; otherwise, executing the step 2.4;
step 2.3.2: if W epsilon [ W ] exists * ,W # ]If the formula (1) is not established, restarting the step (2); otherwise, executing the step 2.4; step 2.3.3: if W exists, W' E [ W ] * ,W # ]So that the following is true
Figure FDA0001841476620000024
Wherein the method comprises the steps of
Figure FDA0001841476620000025
If the set is empty, restarting the step 2, otherwise executing the step 2.4;
step 2.4: the time window is selected, and the method specifically comprises the following steps:
step 2.4.1: sequentially checking w= { W * …,1}, if the formula (1) is not established, setting W o =w+1; otherwise W o ＝1；
Step 2.4.2: sequentially checking w= { W * ,…,W o If W' ∈ (W, W) # ]So that the expression (2) is established, reset W o =w+1; step 2.4.3: for W= { W * ,…,W o Calculation of
Figure FDA0001841476620000026
Figure FDA0001841476620000027
Step 2.4.4: sequentially checking w= { W * ,…,W o If W' ∈ (W, W) # ]So that the following formula is established, reset W o ＝W+1；
Figure FDA0001841476620000028
Step 2.5: the intermittent fault occurrence and disappearance time deducing specifically comprises the following steps:
step 2.5.1: final estimation mu of intermittent fault occurrence and disappearance time q ,
Figure FDA0001841476620000029
And v q ,/>
Figure FDA00018414766200000210
I.e.
Figure FDA00018414766200000211
Step 2.5.2: deducing that the intermittent fault is in
Figure FDA00018414766200000212
Occurs within the moment>
Figure FDA00018414766200000213
And vanishes in time. />
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