CN113189484A

CN113189484A - Digital signal cutting method

Info

Publication number: CN113189484A
Application number: CN202110473292.0A
Authority: CN
Inventors: 林冠玮; 陈姵伃
Original assignee: AU Optronics Corp
Current assignee: AU Optronics Corp
Priority date: 2020-10-29
Filing date: 2021-04-29
Publication date: 2021-07-30
Anticipated expiration: 2041-04-29
Also published as: TW202217334A; TWI749837B; CN113189484B

Abstract

The present disclosure provides a digital signal slicing method, comprising the following steps. A digital signal is received, and a signal characteristic segment is extracted from the digital signal. The step of extracting the signal feature segment includes: calculating an average of a first sum of squares of the digital signal starting from a first time point within a short time window; calculating an average of a second sum of squares of the digital signal starting from the first time point within a long time window; calculating a first ratio of the average of the first sum of squares to the average of the second sum of squares; and judging whether the signal characteristic segment is extracted from the first time point or not according to the first ratio.

Description

Digital signal cutting method

Technical Field

The present disclosure relates to digital signal cutting methods, and more particularly, to a continuous digital signal cutting method.

Background

Current automated equipment technology can utilize a fault Prediction and Health Management (PHM) system to determine whether an actuator in an automated equipment is operating abnormally based on a current signal of the actuator.

Disclosure of Invention

The present disclosure provides a digital signal slicing method. The digital signal cutting method comprises the following steps. Receiving a digital signal and extracting a signal characteristic segment from the digital signal. Wherein the step of extracting the signal feature fragment comprises: calculating an average of a first sum of squares starting from the first point in time within a short time window by the digital signal; calculating an average of a second sum of squares of the digital signal starting within a long time window from the first point in time; calculating a first ratio of the mean of the first sum of squares to the mean of the second sum of squares; and judging whether the signal feature segment is extracted corresponding to the first time point or not according to the first ratio.

In summary, in the technology of the automation device, whether the operation of the actuator (e.g., the motor) is normal or not can be judged by the current signal of the actuator during operation. However, the current signal detected from the actuator includes continuous current changes of the actuator in an operating state and an idle state, wherein the idle current signal is generally less relevant to the determination of whether the operation is normal. Therefore, the present disclosure uses long-and-short-time distance average to extract the signal of the motor in operation, thereby extracting effective feature segments from the signal.

Drawings

The foregoing and other objects, features, and advantages of the disclosure will be apparent from the following more particular description of the embodiments, as illustrated in the accompanying drawings in which:

fig. 1 is a diagram of an apparatus according to some embodiments of the present disclosure.

Fig. 2 is a flow chart of a digital signal slicing method according to some embodiments of the present disclosure.

Fig. 3 is a waveform diagram of a digital signal at a point in time for calculating a long-time-distance average according to some embodiments of the present disclosure.

Fig. 4 is a flow chart of determining whether to extract a signal feature segment from a selected time point in a digital signal segmentation method according to some embodiments of the present disclosure.

Fig. 5 is a waveform diagram illustrating calculation of a long-and-short-term average of the digital signal of fig. 3 at a point in time.

Fig. 6 is a waveform diagram illustrating calculation of a long-and-short-term average of the digital signal of fig. 3 at a point in time.

Fig. 7 is a waveform diagram illustrating calculation of a long-and-short-term average of the digital signal of fig. 3 at a point in time.

Fig. 8 is a waveform diagram illustrating calculation of a long-and-short-term average of the digital signal of fig. 3 at successive time points.

Description of reference numerals:

in order to make the above and other objects, features, advantages and embodiments of the present disclosure more comprehensible, the following symbols are provided:

110: automation device

112: actuator

114: current sensor

120: electronic device

122: processor with a memory having a plurality of memory cells

124: switching circuit

126: memory device

S11, S12, S13, S20, S21, S22, S23, S24, S24E, S240, S241, S242, S2411, S2412, S2421, S2422: step (ii) of

STA: short time window

LTA: long-time window

STA/LTA: long and short time interval average

T1, T2, T3, T4: point in time

Detailed Description

The following embodiments are described in detail with reference to the accompanying drawings, which are not intended to limit the scope of the disclosure, but rather are described in terms of their structural operation, which is not intended to limit the order of execution, and any structures described in connection with elements that are subcombinations of the elements, which produce an equivalent technical effect, are intended to be encompassed by the present disclosure. In addition, the drawings are for illustrative purposes only and are not drawn to scale. For ease of understanding, the same or similar elements will be described with the same reference numerals in the following description.

The term (terms) used throughout the specification and claims has the ordinary meaning as commonly understood in each term used in the art, in the disclosure herein, and in the specific context, unless otherwise indicated.

Furthermore, as used herein, the terms "comprising," including, "" having, "" containing, "and the like are open-ended terms that mean" including, but not limited to. Further, as used herein, "and/or" includes any and all combinations of one or more of the associated listed items.

When an element is referred to as being "coupled" or "coupled," it can be referred to as being "electrically coupled" or "electrically coupled. "coupled" or "coupling" may also be used to indicate that two or more elements are in mutual engagement or interaction. Moreover, although terms such as "first," "second," …, etc., may be used herein to describe various elements, these terms are used merely to distinguish one element or operation from another element or operation described in similar technical terms.

Referring to fig. 1, fig. 1 is a diagram of an apparatus according to some embodiments of the present disclosure. Fig. 1 includes an automation device 110 and an electronic apparatus 120. The automation device 110 includes an actuator 112 and a current sensor 114. The electronic device 120 includes a processor 122, a conversion circuit 124, and a memory 126.

Various goods are assembled or handled on a factory line using automated equipment 110, such as robotic arms or transporters. An actuator 112 (e.g., a motor) is used to drive the automation device 110. For example, the motor powers a robotic arm or a transportation device. Since the actuator 112 of the automation device 110 may be abnormal due to the continuous manufacturing process in the production line, whether the actuator 112 of the automation device 110 is operating normally can be known by determining whether the current signal of the actuator 112 of the automation device 110 is abnormal during the operation period through a fault Prediction and Health Management (PHM) system. However, the current signal detected from the actuator 112 encompasses a continuous signal of the actuator 112 when running and idle.

The judgment of whether the actuator 112 is operating normally mainly considers the current change during the operation period of the actuator 112, and is usually less correlated with the current change at idle.

In the embodiment of the disclosure, the continuous signals of the actuator 112 in the running period and the idle period are cut according to the Long-Time-distance Average STA/lta (short Time Average Over Long Time Average), so as to extract the signal of the actuator 112 in the running period. For a better understanding of how to cut the continuous signal according to the long-and-short-time average STA/LTA, the following embodiment will be described in detail. Referring to fig. 2, fig. 2 is a flowchart of a digital signal slicing method according to some embodiments of the present disclosure.

In step S11, the current sensor 114 detects a current signal of the actuator 112. And the current signal is transmitted by the current sensor 114 to the electronic device 120. In the examples of the present disclosure, the current signal to be processed is from the actuator 112 of the automation device 110. However, the current signal may also be from a power supply, a backlight driving circuit, or a display circuit. Accordingly, the disclosure is not so limited.

In step S12, the current signal is converted into a digital signal. In detail, the electronic device 120 receives the current signal from the current sensor 114, and converts the current signal (usually, an analog signal with continuous current magnitude variation) detected by the current sensor 114 into a digital signal by using the conversion circuit 124, thereby storing the digital signal in the memory 126 of the electronic device 120. For example, the conversion circuit 124 of the electronic device 120 receives the continuous current signal of the actuator 112 from the current sensor 114 within one hour, and converts the received continuous current signal into a digital signal through the conversion circuit 124, whereby the electronic device 120 can store the digital signal to the memory 126.

The determination of whether the automation device 110 is operating properly primarily takes into account the current change during the operation of the actuator 112, which is generally less correlated to the current change during idle operation. If the signal characteristic segment corresponding to the operation time period of the actuator 112 in the digital signal can be obtained, and the invalid segment (or the low-correlation segment) corresponding to the idle time period of the actuator 112 in the digital signal is filtered, the calculation time can be saved and the space of the memory 126 or the data transmission bandwidth occupied by the calculation method can be reduced when the calculation method for determining whether the operation is normally performed is subsequently performed with respect to the automation device 110.

In step S13, the processor 122 receives the aforementioned digital signal. In one embodiment, the processor 122 may read a previously stored digital signal from the electronic device memory 126 (e.g., the content may include a current change in which the actuator 112 is operated for one hour), and in another embodiment, the processor 122 may also receive the digital signal directly from the conversion circuit 124. And, the processor 122 continues to execute steps S20 to S24 to obtain the signal characteristic segment with current variation in the digital signal.

In steps S20 to S24, the short time window STA square and average, the long time window LTA square and average, and the ratio of the short time window STA square and average to the long time window LTA square at the same time point of the digital signal are calculated according to the long-short time interval average STA/LTA. In detail, a long time window LTA is set, a short time window STA is taken within the long time window LTA, and the short time window STA is set to coincide with the start point of the long time window LTA. Thus, by moving the long time window LTA and the short time window STA in the long time window LTA to each time point of the digital signal, the ratio of the square and average of the short time window STA at each time point of the digital signal to the square and average of the long time window LTA can be calculated, which is also called long-short-time-distance average STA/LTA. In other embodiments, the calculation of the digital signal at each time point in the long time window LTA and the short time window STA is performed continuously, so that a linear function of the long-time-distance average STA/LTA of the obtained digital signal can be calculated. If the signal feature segment is extracted from the selected time point according to the long-short-distance average STA/LTA determination, step S24E is executed to extract the signal feature segment.

For a better understanding of how to calculate the long-and-short-term average STA/LTA, please refer to FIG. 3. Fig. 3 is a waveform diagram of a digital signal at a time point T1 for calculating a long-short-term average STA/LTA according to some embodiments of the present disclosure. In fig. 3, the horizontal axis represents time, and the vertical axis represents the amplitude of the digital signal. The amplitude and waveform of the digital signal can be understood as the mode and state corresponding to the operation of the actuator 112.

In an embodiment of the present disclosure, the short time window STA is implemented with a time window length of 0.2 seconds and the long time window LTA is implemented with a time window length of 1.0 second. However, the short time window STA length need only be less than the long time window LTA length. And, if the short time window STA is shorter than the long time window LTA, the sensitivity to the minute amplitude is higher; the larger the short time window STA length compared to the long time window LTA length, the lower the sensitivity to small amplitudes. Therefore, the short time window STA and the long time window LTA may be implemented by other time lengths, which the disclosure is not limited thereto.

In step S20, a time point T1 is selected from the digital signal.

In step S21, the average of the sum of squares starting from the time point T1 within the short time window STA by the digital signal is calculated. Mean of the first sum of squares within a short time window STA

ns is the time window length of the short time window STA, cf (j) is the value of the digital signal at the time point j, and cf (j) is the square sum of the values of the digital signal at the time point j in the present disclosure.

In other words, the digital signal starts from the time point T1 to the average value of the first sum of squares within the short time window STA, which represents the magnitude of signal change of the digital signal in the interval adjacent to the time point T1. When the average value of the first sum of squares within the short time window STA is larger, it represents that the digital signal changes more strongly in the interval adjacent to the time point T1, which means that the time point T1 has a higher possibility of corresponding to the operation period of the motor. On the other hand, the smaller the average of the first sum of squares in the short time window STA, the smaller the variation of the digital signal in the interval adjacent to the time point T1, which means that the time point T1 is more likely to correspond to the idle period of the motor.

For example, the digital signal starts from the time point T1 with a square sum of 0.16 in the short time window STA, and the average of the square sums in the short time window STA at the time point T1 is STA (T1) ═ 0.16/0.2 ═ 0.8. In this example, the short time window STA at time T1 has a small amount of change in the digital signal, and therefore the average value of the sum of squares in the short time window STA is low.

In step S22, the digital message is calculatedThe number starts from time point T1 with the average of the sum of squares within the long time window LTA. Mean value of the sum of squares starting within the long time window LTA from the time point T1

nl is the length of the long time window LTA, CF (j) is the value of the digital signal number at the time point j, and CF (j) is the square sum of the digital signal number at the time point j in the present disclosure. For example, the digital signal starts from time T1 with a first sum of squares of 1 in the long time window LTA, and the average of the sum of squares of 1 in the long time window LTA at time T1 is LTA (T1) 1/1.0 1. In other words, the digital signal begins at time T1 with the average of the second sum of squares within the long time window LTA, representing the average signal change magnitude over an overall longer time range of the digital signal (e.g., corresponding to a change in current to the actuator 112).

That is, the average of the second sum of squares within the long time window LTA represents the average size of the digital signal over a long time. For example, when the automation device 110 ages or is in a high temperature environment, causing an abnormality in the current signal of the actuator 112, it is assumed that the current signal measured by the current sensor 114 is increased (or decreased, as the case may be), for a long time, that is, the average of the second sum of squares within the long time window LTA reflects the overall offset change of the digital signal due to the surrounding factors.

In step S23, the long-and-short-term average STA/LTA is calculated. That is, the ratio of the average of the sum of squares within the short time window STA to the average of the sum of squares within the long time window LTA at the time point T1 is calculated,

the former formula is equal to 0.8/1 ═ 0.8. The long-and-short-term average STA/LTA represents the magnitude of the change in the digital signal in the short-term window STA compared to the digital signal in the long-term window LTA, i.e., the instantaneous change in current of the actuator 112 at a certain point in time relative to the change over time after a certain point in time. In other words, even after a certain point of time, the amplitude of the shifted digital signal becomes significantly larger due to the characteristic deviation of the current sensor 114 (e.g., actuation)The digital signal is enhanced due to aging or damage of the mechanism) at this time point, the short time window STA squared and the average value and the long time window LTA squared and the average value are affected at the same time and are increased accordingly, so that the ratio of the short time interval average STA/LTA to the short time interval average STA/LTA is not affected too much, and the value is still within a certain range.

For example, after a certain time point, the value range of the amplitude oscillation of the digital signal due to the environmental factors becomes large, so that the sum of squares of the short time windows STA is 0.8, and the average value is 0.8/0.2 — 4; the long time window LTA is 5 squared and 5/1-1 averaged. The average of the sum of squares of the short time windows STA divided by the average of the sum of squares of the long time windows LTA is 4/5 ═ 0.8. Therefore, after dividing the short time window STA squared and average by the long time window LTA squared and average, a long-short range average STA/LTA within a particular range may be obtained. Therefore, even if the amplitude oscillation range of the digital signal changes due to environmental factors (for example, the digital signal originally oscillates between 0 to 10mA, changes to oscillate between 0 to 100mA, or changes to oscillate between 0 to 5 mA), the ratio of the long-time interval average STA/LTA at these time points will not change greatly with the change of the amplitude range of the digital signal.

In some embodiments, to extract the signal of the actuator 112 during the operating period from the continuous current signal, the signal of the actuator 112 of the automation device 110 during the operating period is extracted by calculating a root mean square value of the continuous signal and setting a threshold value according to the root mean square value. However, in some cases, environmental factors change (e.g., abnormal operation of the actuator 112 or after maintenance or part replacement of the automation device 110), which may result in a large increase in the oscillation range of the current signal amplitude of the actuator 112. That is, even if the actuator 112 is in the idle state, the current signal will have a certain oscillation amplitude, and the current signal may be extracted by using the previously set threshold value when the actuator 112 is in the idle state, which requires additional labor to reset the threshold value.

In the embodiment of the disclosure, since the long-time-interval average STA/LTA is not affected by the large amplitude variation of the digital signal, it can be determined in real time whether to extract the digital signal without adjusting the threshold (or threshold) additionally. For better understanding of how to judge whether to extract the digital signal in real time, the following embodiments will be described in detail.

In step S24, it is determined whether a signal feature segment is extracted from the selected time point T1 according to the long-short-time-distance average STA/LTA. To determine whether to extract a signal feature segment at time T1, please refer to fig. 4. Fig. 4 is a flow chart of determining whether to extract a signal feature segment from a selected time point in a digital signal segmentation method according to some embodiments of the present disclosure. Steps S240, S241, S242, S2411, S2412, 2424 and S2422 in the step S24 may be executed by the processor 122 of the electronic device 120.

In step S240, whether the digital signal is extracted as a signal feature segment from the signal before the selected time point T1. Assuming that the digital signal before the time point T1 is not extracted as a signal feature segment, step S241 is performed.

In step S241, whether the long-short distance average STA/LTA is greater than or equal to a first threshold. In some embodiments, the first threshold may be set to 1.75, for example. The time interval T1 is 0.8 from the average STA/LTA, which is less than the first threshold of 1.75. Therefore, the step S2411 is continued, and the signal feature segment is maintained not to be extracted. That is, the current signal measured by the current sensor 114 from the time point T1 is the signal when the actuator 112 is idle, so that the digital signal is not extracted as the signal feature segment at the time point T1. Then, the process proceeds to step S20.

In step S20, a time point T2 is selected from the digital signal. Referring to fig. 5, fig. 5 is a waveform diagram illustrating the digital signal of fig. 3 at a time point T2 for calculating the long-short-term average STA/LTA.

In step S21, the average of the sum of squares starting from the time point T2 within the short time window STA by the digital signal is calculated. The digital signal starts from a time point T2 with a square sum of 0.36 in the short time window STA, and the average of the square sums in the short time window STA at a time point T2 is STA (T2) ═ 0.36/0.2 ═ 1.8.

In step S22, the average of the sum of squares starting from the time point T2 within the long time window LTA by the digital signal is calculated. The digital signal starts from time T2 with a sum of squares of 1 in the long time window LTA, and the average of the sum of squares in the long time window LTA at time T2 is LTA (T2) ═ 1/1.0 ═ 1.

In step S23, the long-and-short-term average STA/LTA is calculated. That is, the ratio of the average of the sum of squares within the short time window STA to the average of the sum of squares within the long time window LTA at the time point T2 is calculated,

the former formula is equal to 1.8/1 ═ 1.8. To determine whether to extract a signal feature segment from the selected time point T2 based on the long-and-short-term average STA/LTA. The execution continues with step S240 among step S24. In step S240, whether the digital signal is extracted as a signal feature segment from the signal before the selected time point T2. The digital signal at the time point T1 before the time point T2 is not extracted as a signal feature fragment, and thus step S241 is performed.

In step S241, whether the long-short distance average STA/LTA is greater than or equal to a first threshold. In detail, the first threshold is a product of the long-time-distance average STA/LTA and a specific parameter. According to an example of a short time window STA (0.2 seconds) and a long time window LTA (1 second) of an embodiment in the present disclosure, the maximum value of the historical long and short time interval average STA/LTA is about 2.5. Moreover, setting the specific parameter to 0.7 can avoid extracting noise or missing segments of the extracted signal features. Therefore, the first threshold is set at 2.5 × 0.7 — 1.75. In the present disclosure, the first threshold is not limited to the above-mentioned fixed value, such as 1.75. In other embodiments, the first threshold is determined by the product of the maximum of the historical long-and-short-term average STA/LTA and the parameter, and if the first threshold is set to be too large, the signal with smaller amplitude cannot be interpreted; if the first threshold is set too small, it will be too sensitive to the microseismic signal. Thus, the aforementioned parameters may be set between 0.5 and 0.9.

The duration-time average STA/LTA at time T2 is equal to 1.8, which is greater than the first threshold of 1.75. Therefore, continuing to step S2412, extraction of signal feature segments is started. That is, the current signal measured by the current sensor 114 from the time point T2 is the signal when the actuator 112 is operating, so that the digital signal is extracted as the signal feature segment from the time point T2. Then, the process proceeds to step S20.

Referring to fig. 3 and 4 together, the digital signal does not contain significant amplitude variation in the short time window STA beginning at time T1 in fig. 3, so the long-short-time-interval average STA/LTA value counted from time T1 in fig. 3 is small, such as 0.8 in the previous embodiment. In contrast, the digital signal already contains significant amplitude variations in the short time window STA beginning at time T2 in fig. 4, so the time interval average STA/LTA value counted from time T2 in fig. 4 is relatively large, such as 1.8 in the previous embodiment. Therefore, at time T2, the extraction of digital signals is started according to the variation of the time interval average STA/LTA.

In step S20, a time point T3 is selected from the digital signal. Referring to fig. 6, fig. 6 is a waveform diagram illustrating the digital signal of fig. 3 at a time point T3 for calculating the long-short-term average STA/LTA.

In step S21, the average of the sum of squares starting from the time point T3 within the short time window STA by the digital signal is calculated. The digital signal starts from a time point T3 with a sum of squares within the short time window STA of 0.16, and an average of the sum of squares within the short time window STA of a time point T3 is STA (T3) ═ 0.16/0.2 ═ 0.8.

In step S22, the average of the sum of squares starting from the time point T3 within the long time window LTA by the digital signal is calculated. The digital signal starts from time T3 with a sum of squares of 0.8 in the long time window LTA, and the average of the sum of squares in the long time window LTA at time T3 is LTA (T3) ═ 0.8/1 ═ 0.8.

In step S23, the long-and-short-term average STA/LTA is calculated. That is, the ratio of the average of the sum of squares within the short time window STA to the average of the sum of squares within the long time window LTA at the time point T3 is calculated,

the former formula is equal to 0.8/0.8 ═ 1. To determine whether to extract a signal feature segment from the selected time point T3 based on the long-and-short-term average STA/LTA. Proceed to step S24Step S240. In step S240, whether the digital signal is extracted as a signal feature segment from the signal before the selected time point T3. The digital signal at the time point T2 before the time point T3 has been extracted as a signal feature fragment, and execution continues to step S242.

In step S242, whether the long-short-range average STA/LTA is less than or equal to the second threshold. In some embodiments, for example, the second threshold may be set to 0.4. Setting the second threshold at 0.4 prevents early termination of signal extraction or extraction of idle period signals. The duration-time average STA/LTA at time point T3 is 1, which is greater than the second threshold value of 0.4. Thus, continuing to step S2421, the extraction of the signal feature segments is maintained. That is, the current signal measured by the current sensor 114 from the time point T3 is the signal when the actuator 112 is operating, so the digital signal is continuously extracted from the time point T2 to the time point T3 as the signal feature segment. Then, the process proceeds to step S20. In step S20, a time point T4 is selected from the digital signal. Referring to fig. 7, fig. 7 is a waveform diagram illustrating the digital signal of fig. 3 at a time point T4 for calculating the long-short-term average STA/LTA.

In step S21, the average of the sum of squares starting from the time point T4 within the short time window STA by the digital signal is calculated. The digital signal starts from a time point T4 with a sum of squares of 0.01 in the short time window STA, and the sum of squares and an average value of STA (T4) in the short time window STA at a time point T4 is 0.01/0.2 and 0.05.

In step S22, the average of the sum of squares starting from the time point T4 within the long time window LTA by the digital signal is calculated. The digital signal starts from time T4 with a sum of squares of 0.4 in the long time window LTA, and the sum of squares and an average value LTA (T4) of 0.4/1.0 and 0.4 in the long time window LTA at time T3.

In step S23, the calculation of the long-short-term average is performed. That is, the ratio of the average of the sum of squares within the short time window STA to the average of the sum of squares within the long time window LTA at the time point T4 is calculated,

the former formula is equal to 0.05/0.4 ═ 0.125. To adjust according to the length-time intervalThe STA/LTA determines whether to extract a signal feature fragment from the selected time point T4. The execution continues with step S240 among step S24. In step S240, whether the digital signal is extracted as a signal feature segment from the signal before the selected time point T4. The digital signal at the time point T3 before the time point T4 has been extracted as a signal feature fragment, and execution continues to step S242.

In step S242, whether the long-short-range average STA/LTA is less than or equal to the second threshold. The time interval T4 is 0.125 from the average STA/LTA, which is less than the second threshold of 0.4. Step S2422 is thus continued.

In step S2422, the extraction of the signal feature segment is stopped. That is, the current signal measured by the current sensor 114 from the time point T2 to T4 is a signal when the actuator 112 is operating, and thus the digital signal is extracted as the signal feature segment from the time point T2 until the time point T4 stops. In the present disclosure, the second threshold is not limited to the above-mentioned fixed value, such as 0.4. In other embodiments, if the second threshold is set too large, the signal extraction is terminated early; if the second threshold is set too small, it may cause the step S242 not to continue to the step S2422, resulting in the failure to normally extract the desired segment. Therefore, the second threshold value may be set between 0.3 and 0.6. The second threshold is typically set to less than 0.6, which would otherwise cause the signal extraction to end early.

Referring to fig. 5 and 6, since the digital signal includes a significant amplitude change in the short time window STA starting from the time point T3 in fig. 5, the long-short-time-interval average STA/LTA value counted from the time point T3 in fig. 5 is larger, such as 1.8 in the previous embodiment. In contrast, the digital signal does not include significant amplitude variation in the short time window STA beginning at time T4 in fig. 6, so the time-averaged STA/LTA value from time T4 in fig. 6 is relatively small, such as 0.125 in the previous embodiment. Therefore, at time T4, the extraction of digital signal is stopped according to the variation of the time interval average STA/LTA.

In the above embodiments, the time points T1, T2, T3 and T4 were chosen only to make it clear how to calculate the long-and-short-term average STA/LTA. In practical applications, the time points should be selected at shorter time intervals, so that the time intervals of the individual time points are smaller.

Please refer to fig. 8. Fig. 8 is a waveform diagram illustrating calculation of the long-and-short-term average STA/LTA of the digital signal of fig. 3 at successive time points. In fig. 8, the lower waveform diagram is a digital signal, the vertical axis represents amplitude, and the horizontal axis represents time. The upper linear function in fig. 8 is a linear function of the digital signal calculated by the long-short time interval average STA/LTA, the vertical axis represents the long-short time interval average STA/LTA, and the horizontal axis represents time. Also, the broken lines in the figure represent the first threshold TH1 (e.g., 1.75) and the second threshold TH2 (e.g., 0.4), respectively. By calculating the long-short-distance average STA/LTA of the digital signal at selected time points at shorter time intervals (e.g., at selected time points every 0.002 seconds), the variation of the long-short-distance average STA/LTA with time as shown in fig. 8 can be obtained. And determines whether the long-and-short-term average STA/LTA is greater than a first threshold TH1 or less than a second threshold TH2 to extract a feature segment of the actuator 112 in real time while it is running. The halftone dot background portion shown in fig. 8 is a range in which the digital signal is extracted as a signal feature segment.

In summary, the present disclosure calculates a digital signal converted from the current of the actuator 112 (e.g., a motor) of the automation device 110 by using the long-short-time-distance average STA/LTA, and determines whether the long-short-time-distance average STA/LTA is greater than a first threshold or less than a second threshold, so as to extract a signal characteristic segment of the actuator during operation in real time without being affected by a large change in an oscillation range of the current signal of the actuator 112.

Although the present disclosure has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the disclosure, and therefore, the scope of the disclosure should be limited only by the appended claims.

Claims

1. A method for slicing a digital signal, comprising:

receiving a digital signal; and

extracting a signal feature segment from the digital signal, wherein extracting the signal feature segment comprises:

calculating an average of a first sum of squares of the digital signal starting from a first time point within a short time window;

calculating an average of a second sum of squares of the digital signal starting from the first time point within a long time window;

calculating a first ratio of the average of the first sum of squares to the average of the second sum of squares; and

and judging whether the signal feature segment is extracted corresponding to the first time point or not according to the first ratio.

2. The method of claim 1, wherein determining whether to extract the signal feature segment from the first time point according to the first ratio comprises:

if the digital signal before the first time point is not extracted as the signal characteristic segment and the first ratio is greater than a first threshold value, extracting the signal characteristic segment from the first time point; and

and stopping extracting the signal characteristic segment from the first time point if the digital signal before the first time point is extracted as the signal characteristic segment and the first ratio is smaller than a second threshold value.

3. The method of claim 2, wherein determining whether to extract the signal feature segment from the first time point according to the first ratio comprises:

if the digital signal before the first time point is not extracted as the signal characteristic segment and the first ratio is smaller than the first threshold value, the signal characteristic segment is not extracted; and

if the digital signal before the first time point is extracted as the signal feature segment and the first ratio is greater than the second threshold, the signal feature segment is kept extracted.

4. The method of claim 1, wherein extracting the signal feature segment further comprises:

calculating an average of a third sum of squares of the digital signal starting within the short time window from a second time point;

calculating an average of a fourth sum of squares of the digital signal starting from the second time point within the long time window;

calculating a second ratio of the average of the third sum of squares to the average of the fourth sum of squares; and

and judging whether to extract the signal feature segment from the second time point or not according to the second ratio.

5. The digital signal cutting method of claim 1, further comprising:

receiving a current signal from an actuator through a current sensor, wherein the actuator is used for driving an automatic device to operate; and

converting the current signal into the digital signal.

6. The method according to claim 2, wherein the first threshold is determined by multiplying a parameter by a maximum value of a historical long-short distance average.

7. The method of claim 6, wherein the parameter is between 0.5 and 0.9.

8. The method of claim 2, wherein the second threshold is between 0.3 and 0.6.