Analog quantity signal online anomaly detection method, electronic equipment and readable storage medium

Abstract

The invention discloses an analog quantity signal online anomaly detection method, which adaptively generates an upper limit and a lower limit of an analog quantity signal according to historical data and real-time acquired data, discovers abnormal overrun, trend and fluctuation conditions in the data through signal decomposition, realizes online anomaly detection of the analog quantity signal, improves the accuracy and fineness of anomaly detection, and improves the detection accuracy and accuracy. And finally, corresponding alarm is given according to an abnormal detection result, and the health condition of the equipment is displayed in real time, so that field staff can quickly position a fault source, thereby processing the fault as soon as possible and ensuring the safe operation of the train and the signal equipment.

Description

Technical Field

[0001] The present invention relates to the field of analog signal anomaly detection, and in particular to an online anomaly detection method for analog signal based on decomposition. Background Art

[0002] Rail transit signal equipment is the basic equipment of rail transit signal and train control system, and its normal operation is an important guarantee for the safe operation of trains. Due to the large number of rail transit signal equipment and the complex and changeable operating environment, it is necessary to monitor rail transit signal equipment in real time to detect abnormal operating conditions in time.

[0003] The data collected by rail transit signal equipment monitoring are mostly analog signals. The existing anomaly detection technology manually sets the upper and lower limits of the analog quantity and identifies anomalies, trends and fluctuations in the signal by observation.

[0004] Affected by the different operating environments and service life of the equipment, the same monitoring quantity of the same type of equipment will still show different characteristics in different areas or time periods. Therefore, the upper and lower limits of many analog signals need to be set based on manual experience and modified in real time according to the different operating environments and time periods. At the same time, manual monitoring is used to detect anomalies, resulting in a high manual load and prone to misjudgment or missed judgment.

[0005] The statements herein merely provide background art related to the present invention and do not necessarily constitute prior art. Summary of the invention

[0006] The purpose of the present invention is to provide an analog signal online anomaly detection method, which realizes the online anomaly detection of analog signals, improves the accuracy and precision of anomaly detection, facilitates on-site staff to quickly locate the source of the fault, and ensures the safe operation of trains and signal equipment.

[0007] In order to achieve the above object, the present invention provides an analog signal online abnormality detection method, comprising the following steps:

[0008] Step S1, generating a signal data set to be analyzed and a reference benchmark signal data set, wherein the signal data set to be analyzed and the reference benchmark signal data set constitute original signal data;

[0009] The analog signal source is collected to obtain n data to form a signal data set to be analyzed, which is recorded as X = [x1, x2, ..., x n ];

[0010] Select normal historical data of any time period with the same collection items as the data in the data set to be analyzed to form a reference benchmark signal data set, denoted as XH = [xh1, xh2, ..., xhm ], the reference benchmark signal data set has m data;

[0011] Step S2, decomposing the original signal data to obtain a trend component and a residual component;

[0012] The trend component of the signal data set X to be analyzed is denoted by The residual component of the signal data set X to be analyzed is denoted by

[0013] The trend component of the reference signal data set XH is denoted as The residual component of the reference signal data set XH is denoted as

[0014] Step S3, performing statistical calculations on the original signal data and its trend component and residual component;

[0015] Calculate the mean and standard deviation of the original signal data and its trend component and residual component respectively, where the signal data to be analyzed X and the trend component X of the signal data to be analyzed are T , the residual component X of the signal data to be analyzed R The mean values ​​of The standard deviation is denoted as X std , Reference signal data XH, trend component of reference signal data XH T , the residual component XH of the reference signal data R The mean values ​​of The standard deviation is denoted as XH std ,

[0016] Step S4, calculating the normal upper limit and normal lower limit of the signal data to be analyzed and its trend component and residual component;

[0017] Assuming that the collected data obeys the normal distribution, the normal upper limit and normal lower limit of the signal data to be analyzed and its trend component and residual component are calculated according to the z-sigma criterion, z∈[2,3];

[0018] Step S5, performing abnormal analysis on the signal data to be analyzed and its trend component and residual component according to the normal upper limit and the normal lower limit;

[0019] Step S6: Send the data anomaly detection result and display an alarm.

[0020] If the time series data sampled in the signal data set to be analyzed is stationary, the larger the value of m is, the higher the accuracy of the algorithm is; if the time series data sampled in the signal data set to be analyzed is not stationary, m is set to 0.

[0021] In step S2, the method of decomposing and obtaining the trend component and the residual component includes:

[0022] Taking the decomposition of the signal data X to be analyzed as an example, the trend component X is calculated by the sliding window mean filtering algorithm based on the center point. T , the calculation formula is:

[0023]

[0024] Wherein, r is the radius of the sliding window, r is less than n, preferably, n∈[1,n / 3];

[0025] For x i-k , if the subscript index ik < 1, then let x i-k =x1;

[0026] For x i+k , if the subscript index i+k>n, then let x i+k =x n ;

[0027] According to the signal data X to be analyzed and the trend component X T The difference between the residual component X R , the calculation formula is:

[0028]

[0029] In step S3, the method for performing statistical calculation includes:

[0030] To calculate the mean of the signal data X to be analyzed and standard deviation X std For example:

[0031]

[0032] In step S4, the method for calculating the normal upper limit and the normal lower limit of the signal data to be analyzed and its trend component and residual component includes:

[0033] The upper limit of the normal value of the signal data X to be analyzed is denoted as ub z , the lower limit of normal is denoted as lb z ;

[0034]

[0035] The trend component X of the signal data to be analyzed T The upper limit of normal is recorded as The lower limit of normal

[0036]

[0037] The residual component X of the signal data to be analyzed R The normal upper limit is denoted as The normal lower limit is denoted as

[0038]

[0039] In step S5, the method for performing anomaly analysis includes:

[0040] For the original signal data X to be analyzed:

[0041] If x i < ub2 or x i > lb2, it is determined that the signal to be analyzed does not exceed the limit at time i;

[0042] If ub2 < x i < ub3 or lb3 < x i < lb2, it is determined that the signal to be analyzed is slightly over the limit at time i;

[0043] If x i > ub3 or x i < lb3, it is determined that the signal to be analyzed is severely over the limit at time i;

[0044] For the trend component X of the original signal data to be analyzed T :

[0045] If or It is determined that the signal to be analyzed does not show an abnormal trend at time i;

[0046] If or It is determined that the signal to be analyzed shows a slightly abnormal trend at time i;

[0047] If or It is determined that the signal to be analyzed shows a severely abnormal trend at time i;

[0048] For the residual component X of the original signal data to be analyzed R :

[0049] If or It is determined that the signal to be analyzed does not show abnormal fluctuations at time i;

[0050] If or It is determined that the signal to be analyzed shows slightly abnormal fluctuations at time i;

[0051] If or It is judged that the signal to be analyzed has serious abnormal fluctuations at time i.

[0052] The present invention also provides an electronic device, comprising a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the online anomaly detection method for analog signal is implemented.

[0053] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the online abnormality detection method for analog signal is implemented.

[0054] The present invention adaptively generates the upper and lower limits of analog signals based on historical data and real-time collected data, and discovers abnormal over-limit, trends and fluctuations in the data through signal decomposition, thereby realizing online abnormality detection of analog signals and improving the accuracy and precision of abnormality detection. Finally, corresponding alarms are issued according to the abnormality detection results and the health status of the equipment is displayed in real time, which is convenient for on-site staff to quickly locate the source of the fault, so as to deal with the fault as soon as possible and ensure the safe operation of the train and signal equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of an analog signal online abnormality detection method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following is based on Figure 1 The preferred embodiments of the present invention are described in detail.

[0057] like Figure 1 As shown, the present invention provides an analog signal online abnormality detection method, comprising the following steps:

[0058] Step S1, generating a signal data set to be analyzed and a reference benchmark signal data set, wherein the signal data set to be analyzed and the reference benchmark signal data set constitute original signal data;

[0059] The analog signal source is collected to obtain n data, forming a signal data set to be analyzed with a length of n, denoted as X = [x1, x2, ..., x n ];

[0060] Select normal historical data of any time period with the same collection items as the data in the data set to be analyzed to form a reference benchmark signal data set, denoted as XH = [xh1, xh2, ..., xh m ], the reference benchmark signal data set has m data;

[0061] The same acquisition item means that the data in the dataset to be analyzed and the data in the reference signal dataset come from the same signal source. For example, if X is the voltage value measured at point A, then XH must also be the voltage value measured at point A.

[0062] If the time series data sampled in the signal data set to be analyzed is stationary, then the value of m should be as large as possible. The larger the m, the more historical data is used, and the higher the accuracy of the algorithm is. If the time series data sampled in the signal data set to be analyzed is not stationary, then the larger the m value is, the accuracy of the algorithm will be affected and reduced. In this case, m can be set to 0, that is, no historical information is used.

[0063] Step S2, decomposing the original signal data to obtain a trend component and a residual component;

[0064] Decompose the signal data set X to be analyzed and the reference signal data set XH respectively to obtain the trend component and residual component of the data;

[0065] Among them, the trend component of the signal data set X to be analyzed is recorded as The residual component of the signal data set X to be analyzed is denoted by

[0066] The trend component of the reference signal data set XH is denoted as The residual component of the reference signal data set XH is denoted as

[0067] The same decomposition method is used for the signal data to be analyzed and the reference signal data. Taking the decomposition of the signal data X to be analyzed as an example, the calculation steps are as follows:

[0068] Step S2.1: Calculate the trend component X by using the sliding window mean filtering algorithm based on the center point T , the calculation formula is:

[0069]

[0070] Wherein, r is the radius of the sliding window, r is less than n, preferably, n∈[1,n / 3];

[0071] For x i-k , if the subscript index ik < 1, then let x i-k =x1;

[0072] For x i+k , if the subscript index i+k>n, then let x i+k =x n ;

[0073] Step S2.2: Based on the signal data X to be analyzed and the trend component X T The difference between the residual component XR , the calculation formula is:

[0074]

[0075] Step S3, performing statistical calculations on the original signal data and its trend component and residual component;

[0076] Calculate the mean and standard deviation of the original signal data and its trend component and residual component respectively, where the signal data to be analyzed X and the trend component X of the signal data to be analyzed are T , the residual component X of the signal data to be analyzed R The mean values ​​of The standard deviation is denoted as X std , Reference signal data XH, trend component of reference signal data XH T , the residual component XH of the reference signal data R The mean values ​​of The standard deviation is denoted as XH std ,

[0077] To calculate the mean of the signal data X to be analyzed and standard deviation X std For example:

[0078]

[0079] Step S4, calculating the normal upper limit and normal lower limit of the signal data to be analyzed and its trend component and residual component;

[0080] Assuming that the collected data obeys the normal distribution, the normal upper limit and normal lower limit of the signal data to be analyzed and its trend component and residual component are calculated according to the z-sigma criterion, z∈[2,3];

[0081] Among them, the normal upper limit of the signal data X to be analyzed is recorded as ub z , the lower limit of normal is denoted as lb z ;

[0082]

[0083] The trend component X of the signal data to be analyzed T The upper limit of normal is recorded as The lower limit of normal

[0084]

[0085] The residual component X of the signal data to be analyzed R The upper limit of normal is recorded as The normal lower limit is denoted as

[0086]

[0087] Step S5. Analyze the abnormality of the signal data to be analyzed, its trend component, and residual component based on the normal upper limit and normal lower limit;

[0088] Step S5.1. For the original signal data X to be analyzed:

[0089] If x i <ub2 or x i >lb2, it is determined that the signal to be analyzed does not exceed the limit at time i;

[0090] If ub2 < x i <ub3 or lb3 < x i <lb2, it is determined that the signal to be analyzed is slightly over the limit at time i;

[0091] If x i >ub3 or x i <lb3, it is determined that the signal to be analyzed is severely over the limit at time i;

[0092] Step S5.2. For the trend component X of the original signal data to be analyzed T :

[0093] If or it is determined that the signal to be analyzed does not show an abnormal trend at time i;

[0094] If or it is determined that the signal to be analyzed shows a slightly abnormal trend at time i;

[0095] If or it is determined that the signal to be analyzed shows a severely abnormal trend at time i;

[0096] Step S5.3. For the residual component X of the original signal data to be analyzed R :

[0097] If or it is determined that the signal to be analyzed does not show abnormal fluctuations at time i;

[0098] If or it is determined that the signal to be analyzed shows slightly abnormal fluctuations at time i;

[0099] If or It is judged that the signal to be analyzed has serious abnormal fluctuations at time i;

[0100] Step S6: Send the data anomaly detection result to the intelligent operation and maintenance platform and display an alarm.

[0101] The present invention adaptively generates the upper and lower limits of analog signals based on historical data and real-time collected data, and discovers abnormal over-limit, trends and fluctuations in the data through signal decomposition, thereby realizing online abnormality detection of analog signals and improving the accuracy and precision of abnormality detection. Finally, the judgment results are sent to the intelligent operation and maintenance system to perform corresponding alarms and display the health status of the equipment, so as to facilitate on-site staff to quickly locate the source of the fault, thereby handling the fault as soon as possible and ensuring the safe operation of the train and signal equipment.

[0102] It should be noted that, in the embodiments of the present invention, the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0103] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0104] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0105] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0106] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0107] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0108] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as limiting the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention. After reading the above content, it will be obvious for those skilled in the art to make various modifications and substitutions to the present invention. Therefore, the scope of protection of the present invention should be defined by the attached claims.

Claims

1. A method for online abnormality detection of analog signals, characterized in that: The following steps are involved: Step S1, generating a signal data set to be analyzed and a reference benchmark signal data set, wherein the signal data set to be analyzed and the reference benchmark signal data set constitute original signal data; The analog signal source is collected to obtain n data to form a signal data set to be analyzed, which is recorded as X = [x1, x2, ..., x n ]; Select normal historical data of any time period with the same collection items as the data in the data set to be analyzed to form a reference benchmark signal data set, denoted as XH = [xh1, xh2, ..., xh m ], the reference benchmark signal data set has m data; Step S2, decomposing the original signal data to obtain a trend component and a residual component; The trend component of the signal data set X to be analyzed is denoted by The residual component of the signal data set X to be analyzed is denoted by The trend component of the reference signal data set XH is denoted as The residual component of the reference signal data set XH is denoted as Step S3, performing statistical calculations on the original signal data and its trend component and residual component; Calculate the mean and standard deviation of the original signal data and its trend component and residual component respectively, where the signal data to be analyzed X and the trend component X of the signal data to be analyzed are T , the residual component X of the signal data to be analyzed R The mean values ​​of The standard deviation is denoted as X std , Reference signal data XH, trend component of reference signal data XH T , the residual component XH of the reference signal data R The mean values ​​of The standard deviation is denoted as XH std , Step S4, calculating the normal upper limit and normal lower limit of the signal data to be analyzed and its trend component and residual component; Assuming that the collected data obeys the normal distribution, the normal upper limit and normal lower limit of the signal data to be analyzed and its trend component and residual component are calculated according to the z-sigma criterion, z∈[2,3]; Step S5, performing abnormal analysis on the signal data to be analyzed and its trend component and residual component according to the normal upper limit and the normal lower limit; Step S6: Send the data anomaly detection result and display an alarm.

2. The method for online abnormality detection of analog signals according to claim 1, characterized in that: If the time series data sampled in the signal data set to be analyzed is stationary, the larger the value of m is, the higher the accuracy of the algorithm is; if the time series data sampled in the signal data set to be analyzed is not stationary, m is set to 0.

3. The method for online abnormality detection of analog signals according to claim 1, characterized in that: In step S2, the method of decomposing and obtaining the trend component and the residual component includes: Taking the decomposition of the signal data X to be analyzed as an example, the trend component X is calculated by the sliding window mean filtering algorithm based on the center point. T , the calculation formula is: Wherein, r is the radius of the sliding window, r is less than n, preferably, n∈[1,n / 3]; For x i-k , if the subscript index ik < 1, then let x i-k =x1; For x i+k , if the subscript index i+k>n, then let x i+k =x n ; According to the signal data X to be analyzed and the trend component X T The difference between the residual component X R , the calculation formula is:

4. The method for online abnormality detection of analog signals according to claim 3, characterized in that: In step S3, the method for performing statistical calculation includes: To calculate the mean of the signal data X to be analyzed and standard deviation X std For example:

5. The method for online abnormality detection of analog signals according to claim 4, characterized in that: In step S4, the method for calculating the normal upper limit and the normal lower limit of the signal data to be analyzed and its trend component and residual component includes: The upper limit of the normal value of the signal data X to be analyzed is denoted as ub z , the lower limit of normal is denoted as lb z ; The trend component X of the signal data to be analyzed T The upper limit of normal is recorded as The lower limit of normal The residual component X of the signal data to be analyzed R The upper limit of normal is recorded as The lower limit of normal 6. The method for online abnormality detection of analog signals according to claim 5, characterized in that: In step S5, the method for performing abnormality analysis includes: For the original signal data X to be analyzed: If x i < ub2 or x i > lb2, it is determined that the signal to be analyzed does not exceed the limit at time i; If ub2 < x i < ub3 or lb3 < x i < lb2, then it is determined that the signal to be analyzed is slightly over-limit at time i; If x i > ub3 or x i < lb3, then it is determined that the signal to be analyzed is severely out of limit at time i; For the trend component X of the original signal data to be analyzed T : like or Then it is judged that the signal to be analyzed has no abnormal trend at time i; like or Then it is judged that the signal to be analyzed has a slight abnormal trend at time i; like or Then it is judged that the signal to be analyzed has a serious abnormal trend at time i; For the residual component X of the original signal data to be analyzed R : like or It is judged that the signal to be analyzed has no abnormal fluctuation at time i; like or It is judged that the signal to be analyzed has a slight abnormal fluctuation at time i; like or It is judged that the signal to be analyzed has serious abnormal fluctuations at time i.

7. An electronic device, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the online anomaly detection method for analog signal according to any one of claims 1 to 6 is implemented.

8. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the online anomaly detection method for analog signals as described in any one of claims 1 to 6 is implemented.

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