CN110626900A

CN110626900A - Equipment operation abnormity judgment method

Info

Publication number: CN110626900A
Application number: CN201910896596.0A
Authority: CN
Inventors: 马琪聪; 张嘉祺; 李金鹏; 齐洋
Original assignee: Maoqi Intelligent Technology Shanghai Co Ltd
Current assignee: Jiage Technology (Zhejiang) Co.,Ltd.; Maoqi Intelligent Technology Shanghai Co Ltd
Priority date: 2019-09-23
Filing date: 2019-09-23
Publication date: 2019-12-31
Anticipated expiration: 2039-09-23
Also published as: CN110626900B

Abstract

The invention discloses a method for judging equipment operation abnormity, which comprises the following steps: step S1, acquiring acceleration data of the equipment running in the set direction; filtering the acquired acceleration data, and filtering the data of the equipment in a static state; step S21, generating an acceleration curve in a set direction according to the acceleration data, and intercepting an acceleration curve of an operation action; step S22, dividing the acceleration curve into a plurality of segments according to the slope change and the acceleration direction change of each single group of acceleration curve; and step S23, judging whether the equipment is abnormal or not according to the acceleration characteristics represented in each segmented acceleration curve. The method for judging the abnormal operation of the equipment can acquire the acceleration data of the equipment according to the operation condition of the equipment, can judge whether the equipment operates abnormally or not according to the acceleration data, can judge the equipment fault, can find the abnormal condition which possibly causes the equipment fault, and effectively ensures the use safety of the equipment.

Description

Equipment operation abnormity judgment method

Technical Field

The invention belongs to the technical field of automation equipment, relates to an abnormity judgment system, and particularly relates to an equipment operation abnormity judgment method.

Background

The elevator is the most common vertical transportation vehicle in modern high-rise buildings, saves time and physical strength of people and provides convenience for daily life. As a special device closely related to the life safety of the public, the safe operation of the elevator is receiving more and more attention from the society. However, because the elevator has a complex structure, the need to ensure safe and reliable operation of the elevator and detect the operation state and fault condition of the elevator become urgent needs for elevator management, maintenance and safe operation.

According to the statistics of the information network of the Chinese industry, China is the largest elevator country of production and consumer in the world and is also the largest elevator exit country. 81 ten thousands of newly-added elevators in 2017 in China, and the national elevator holding amount is 562.7 ten thousands.

The elevator industry in China has been developed for 70 years and is quite large at present. In the future, the whole industry will present the following development trends:

(1) domestic elevators will gradually expand the market share; (2) the elevator maintenance and repair market will be gradually standardized and expanded; (3) the elevator supervision will be intelligent.

At present, some enterprises in China develop a plurality of remote monitoring systems, and property, elevator operation companies and government departments can remotely monitor the state of an elevator in real time, find abnormal conditions and can acquire related information in time; however, since these systems are based on wireless network bases such as GPRS/GSM or 3G/4G, the following disadvantages are present:

a. the real-time running information of the elevator collected by the system is transmitted through wireless communication networks such as mobile, communication or telecommunication, so that the data flow is quite large, and in addition, the charging of a wireless network operator is based on the flow, so that the operation cost of the elevator remote monitoring system is high, and 24-hour uninterrupted monitoring cannot be realized.

b. The elevator fault early warning system has simple functions, has no database management function, can only carry out simple elevator running state monitoring, has no maintenance quality management monitoring function, and cannot carry out early warning of elevator faults.

c. The system compatibility is poor, the control can be only carried out on a few elevator types, the elevator faults cannot be accurately analyzed and judged, and specific fault positions of the elevator cannot be accurately given.

The existing monitoring mode generally knows the condition in the elevator by a camera arranged in the elevator or by receiving an alarm signal sent in the elevator; elevator faults cannot be predicted.

In view of the above, nowadays, there is an urgent need to design a new abnormality identification method for elevator and other equipment, so as to overcome the above-mentioned defects existing in the existing monitoring method for elevator and other equipment.

Disclosure of Invention

The invention provides a method for judging the abnormal operation of equipment, which can identify the abnormal condition of the equipment according to acceleration data in the operation process of the equipment and effectively ensure the use safety of the equipment.

In order to solve the technical problem, according to one aspect of the present invention, the following technical solutions are adopted:

an apparatus operation abnormality judgment method includes:

step S1, acquiring acceleration data of the equipment running in the set direction; filtering the acquired acceleration data, and filtering the data of the equipment in a static state;

step S2, judging whether each device has hidden trouble of generating fault according to the acceleration data of the corresponding device; the method specifically comprises the following steps:

step S21, intercepting acceleration data of an operation action according to the acceleration data collected in the set direction;

step S22, dividing the acceleration data of one action into a plurality of segments according to the acceleration data of one action;

and step S23, judging whether the equipment runs abnormally according to the acceleration characteristics represented in each segment acceleration data.

As an embodiment of the present invention, the step S2 specifically includes:

step S21, generating an acceleration curve in a set direction according to the acceleration data, and intercepting an acceleration curve of an operation action;

step S22, dividing the acceleration curve into a plurality of segments according to the slope change and the acceleration direction change of each single group of acceleration curve;

and step S23, judging whether the equipment is abnormal or not according to the acceleration characteristics represented in each segmented acceleration curve.

As an embodiment of the present invention, in step S1, the filtered acceleration data is uploaded to a cloud database; in step S2, the acceleration data of each device is processed by the cloud software, and whether each device has a hidden trouble of generating a fault is determined.

In one embodiment of the present invention, the acceleration data acquired in step S1 is three-axis acceleration, and includes acceleration data in three directions, i.e., a first axis, a second axis, and a third axis.

In step S1, the device is an elevator, and acceleration information of the operation of the elevator is acquired by providing an acceleration sensor in an elevator light curtain.

As an embodiment of the present invention, in step S1, the acquired acceleration data includes first axis direction acceleration data;

in step S2, it is determined whether there is a risk in the operation of the device in the first axis direction according to the first axis direction acceleration data of the corresponding device.

As an embodiment of the present invention, in step S1, the obtained acceleration data is three-axis acceleration, including acceleration data in three directions of a first axis, a second axis, and a third axis; the third axis direction is vertical to a plane formed by the first axis direction and the second axis direction;

and monitoring the vibration of the equipment in the direction perpendicular to a plane formed by the first shaft and the second shaft according to the acceleration data of the corresponding equipment in the third shaft direction, judging whether the transverse vibration amplitude of the equipment is abnormal in the running process of the equipment, and assisting in troubleshooting hidden dangers.

As an embodiment of the present invention, in step S21, the acceleration data includes acceleration data of a plurality of discrete data points, and the acceleration data of the plurality of discrete data points is fitted to an acceleration curve;

acquiring a peak curve and a trough curve according to the acceleration curve; one peak curve and the corresponding trough curve are determined as an action unit, and at least one action unit is taken as an operation action.

As an embodiment of the present invention, the step S21 includes:

step S211: finding out the maximum value and the minimum value of all acceleration signals in the running direction of the equipment, taking the maximum value of the acceleration signals, the minimum value of the acceleration signals and the acceleration signals between the maximum value of the acceleration signals and the minimum value of the acceleration signals as partial data in one running action, and taking the partial data as a first batch of acceleration signals;

step S212: and determining a starting point and an ending point of acceleration data corresponding to one running action according to the change rate of the peripheral acceleration signals of the first group of acceleration signals.

In one embodiment of the present invention, in step S212, the change rate of the acceleration degree before and after the first batch of acceleration signals is obtained, and the point at which the acceleration signals start to change before the first batch of acceleration signals is obtained as the starting point; and acquiring a point of the acceleration signal ending change after the first batch of acceleration signals, and taking the point as a termination point.

As an embodiment of the present invention, the step S21 specifically includes:

step S211, finding a median x1 corresponding to the acceleration data segment data as a third standard line in the acceleration curve graph, wherein x1+ e is a second standard line, and x1+ f is a first standard line;

step S212, if at least one continuous set point appears between the first standard line and the second standard line, a peak or a trough is considered to appear at the point, and the first point of the continuous section is marked as A, and the last point is marked as B;

step S213, obtaining corresponding slopes kA and kB at A, B, obtaining d by using the similarity kA ═ data [ a ] -x1)/d and kB ═ x1-data [ B ])/d, and obtaining that the peak or trough should start from the first point before a and end at the second point after B; wherein data [ A ] represents the data value of point A, and d represents the number of points that are a distance from A, B; kA. kB is obtained by dividing the difference between the forward or backward ith point value of the point A and the point B and the point A and the point B by i, wherein i is a set value;

step S214, after all wave crests and wave troughs are obtained, if two adjacent wave crests in the same direction appear, the two wave crests are determined to belong to two single uplink and downlink operation curves; determining that the wave crests and wave troughs at two ends of the stable data segment with the length exceeding a set threshold belong to two single uplink and downlink operations; combining the peak of a continuous data segment with an adjacent valley located therebehind; the stationary data segment refers to a segment of data in which the absolute value of the acceleration is lower than a set value, and at this time, the acceleration of the device approaches zero.

As an embodiment of the present invention, the step S22 includes:

step S221: calculating the jerk of each point of the corresponding area in one operation action;

step S222: identifying and calculating all area sections with jerk values smaller than a set value on an acceleration curve time axis corresponding to one operation action, and filtering the start area section and the stop area section to obtain 3 area sections which are respectively a uniform acceleration section, a uniform velocity section and a uniform deceleration section;

step S223: dividing the whole area into 7 sections according to the area section obtained in the step S222, and sequentially forming an acceleration increasing section, a uniform acceleration increasing section, an acceleration reducing section, a uniform speed section, an acceleration and deceleration section, a uniform deceleration section and a deceleration reducing section; the acceleration section is an acceleration time period in which acceleration is increased, the deceleration section is an acceleration time period in which acceleration is decreased, the uniform acceleration section is an acceleration time period in which acceleration is not changed, the acceleration section is a deceleration time period in which acceleration is increased, and the deceleration section is a deceleration time period in which acceleration is decreased.

As an embodiment of the present invention, in step S22, it is determined whether the device is going up or down; if the initial change direction of the acceleration signal of the one-time operation is the same as the acceleration direction when the equipment is static, the equipment is judged to be in an uplink state, otherwise, the equipment is judged to be in a downlink state.

As an embodiment of the present invention, in step S1, the acquired acceleration data includes second axis direction acceleration data;

in step S2, it is determined whether there is a hidden danger in the door opening and closing operation process of the device according to the second axial acceleration data of the corresponding device.

As an embodiment of the present invention, in step S22, the acceleration data includes acceleration data of a plurality of discrete data points, and the acceleration data of the plurality of discrete data points is fitted to an acceleration curve; acquiring the curve slope of each key point in the acceleration curve and the acceleration value of each key point; if the positive and negative values of the slope of the curve change or the positive and negative values of the acceleration value change, the changed points are used as segmentation nodes; wherein, the change of the positive value and the negative value comprises any change among a positive value, zero and a negative value.

As an embodiment of the present invention, step S21 includes:

step S211: finding out the maximum value and the minimum value in all the acceleration signals of the second shaft, and combining the maximum value and the minimum value into a door opening or closing area;

step S212: combining the areas found in step S211 to form an open-close door combination;

step S213: and judging whether the adjacent opening-closing door combinations belong to the same door opening and closing movement, and determining all door opening and closing areas.

As an embodiment of the present invention, step S21 includes:

step S211: finding out wave crests and wave troughs in all acceleration signal curves of the second shaft, finding out wave crests and wave troughs of which the distance between the wave crests and the wave troughs is less than a set number of sampling points, taking the corresponding wave crests and wave troughs and acceleration signals between the wave crests and the wave troughs as partial data in a one-time door opening or closing combination, and taking the partial data as a first batch of acceleration signals; the combination of the door opening is wave crest-wave trough or wave trough-wave crest, and the combination of the door closing is wave trough-wave crest or wave crest-wave trough;

step S212: calculating a median y of signal values of the first group of acceleration signals, taking the median y as a third standard line, making a first standard line with the value of y + c parallel to a time axis, a second standard line with the value of y + d, a fourth standard line with the value of y-d, and a fifth standard line with the value of y-c, wherein the first standard line and the fifth standard line are used for filtering extreme points; determining the starting point of the peak through a second standard line, taking N points forward as the starting point of the door opening, determining the ending point of the trough through a fourth standard line, and taking M points backward as the ending point of the door closing; the area from the door opening starting point to the door closing end point is a temporary door opening and closing area; the N points comprise signal values in a short time before the door is opened, and the M points comprise signal points in a short time after the door is closed; wherein c and d are set values;

step S213: because the elevator door is re-opened, the temporary door opening and closing area obtained in step S212 is not necessarily a complete one-time door opening and complete closing process, and all the temporary door opening and closing areas are combined to obtain a real elevator door opening and closing area.

As an embodiment of the present invention, step S22 includes:

step S221: calculating the acceleration of each point in the area;

step S222: identifying and calculating all the area sections with the jerk value smaller than the set value on the time axis to obtain a plurality of area sections, including: the system comprises a gantry crane starting area, a plurality of acceleration change interval areas and a gantry crane stopping area; the acceleration change interval areas at least comprise a first acceleration change interval area and a second acceleration change interval area, and intersection points are formed between curves between the first acceleration change interval area and the second acceleration change interval area and an acceleration median line;

step S223: and dividing the whole operation curve into a plurality of sections according to the door machine starting area, the door machine stopping area, the jerk change interval areas and the intersection obtained in the step S222.

As an embodiment of the present invention, in step S23, the maximum jerk, the average amplitude, the time, and the total maximum velocity, the maximum acceleration, and the value on three axes returned by the acceleration sensor at rest are calculated for each piece of data;

listing all abnormal parameters of the equipment, and presuming which abnormal expressions and the probability of the abnormal expressions possibly occurring in the equipment according to the relation of the equipment parameters, the fault expressions and the fault reasons, and further presuming which faults or hidden dangers possibly occurring or existing in the equipment.

The invention has the beneficial effects that: the method for judging the abnormal operation of the equipment can acquire the acceleration data of the equipment (such as an elevator) according to the operation condition of the equipment, and can judge whether the equipment (such as the elevator) operates abnormally (for example, the time for opening and closing the door is prolonged because the door is opened and closed by a foreign object) according to the acceleration data.

Drawings

Fig. 1 is a flowchart of an apparatus anomaly determination method according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of acceleration curves and standard lines in the uplink and downlink processes of an elevator in one embodiment of the invention.

Fig. 3 is a schematic diagram of a acceleration curve in the process of opening and closing the door of the elevator in one embodiment of the invention.

Fig. 4 is a sectional view of the acceleration curve during the door opening and closing process of the elevator according to an embodiment of the present invention.

Fig. 5 is a schematic diagram of the velocity and acceleration curves of the elevator in the ascending and descending process in one embodiment of the invention.

Fig. 6 is a graph showing the velocity and acceleration curves during the opening or closing of an elevator door according to an embodiment of the present invention.

FIG. 7 is a sectional view of a door opening area according to an embodiment of the present invention.

Fig. 8 is a schematic diagram of uplink region segmentation according to an embodiment of the present invention.

Detailed Description

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

For a further understanding of the invention, reference will now be made to the preferred embodiments of the invention by way of example, and it is to be understood that the description is intended to further illustrate features and advantages of the invention, and not to limit the scope of the claims.

The description in this section is for several exemplary embodiments only, and the present invention is not limited only to the scope of the embodiments described. It is within the scope of the present disclosure and protection that the same or similar prior art means and some features of the embodiments may be interchanged.

In the specification, an "operation action" refers to an action in the operation process of the device, such as an ascending or descending action, a door opening action or a door closing action, or a door opening and closing action.

The invention discloses a method for judging equipment running abnormity, and FIG. 1 is a flow chart of the method for judging equipment running abnormity in one embodiment of the invention; referring to fig. 1, in an embodiment of the present invention, the method for determining an abnormal operation of a device includes:

step S1, acquiring acceleration data of the device running in a set direction; filtering the acquired acceleration data, and filtering the data of the equipment in a static state;

step S2, judging whether each device has hidden danger of generating faults according to the acceleration data of the corresponding device; the method specifically comprises the following steps:

In an embodiment of the present invention, in step S1, the apparatus is an elevator, and the acceleration information of the elevator operation is obtained by providing an acceleration sensor on an elevator light curtain; the method for judging the abnormal operation of the equipment is an elevator abnormal judgment method.

In an embodiment of the present invention, in step S1, the filtered acceleration data is uploaded to a cloud database; in step S2, the acceleration data of each device is processed by the cloud software, and whether each device has a hidden trouble of generating a fault is determined.

In an embodiment of the present invention, in step S1, the obtained acceleration data is three-axis acceleration, including acceleration data in three directions of a first axis, a second axis, and a third axis.

In an embodiment of the present invention, the method of the present invention is used for determining the abnormal operation condition of the elevator, and in step S1, the acceleration information of the operation of the elevator is obtained by installing an acceleration sensor in the elevator light curtain. In another embodiment of the present invention, in step S1, the acceleration sensor may be disposed at other positions of the elevator, not at the elevator light curtain.

In an embodiment of the present invention, in step S1, the obtained acceleration data is three-axis acceleration, including acceleration data in three directions of a first axis, a second axis, and a third axis; the third axis direction is vertical to a plane formed by the first axis direction and the second axis direction; and monitoring the vibration of the equipment in the direction perpendicular to a plane formed by the first shaft and the second shaft according to the acceleration data of the corresponding equipment in the third shaft direction, judging whether the transverse vibration amplitude of the equipment is abnormal in the running process of the equipment, and assisting in troubleshooting hidden dangers.

In one embodiment of the invention, the acceleration sensor chip adopts a BMA421 three-axis acceleration sensor of BOSCH, the measurement precision is about 0.98mg/LSB, and the uniaxial value range of the sensor is +/-2048 LSB. The three-axis acceleration sensor is additionally arranged on the elevator light curtain, the X axis collects the up-down acceleration data of the elevator, the Y axis collects the door opening and closing data, and the Z axis collects the data of the elevator car in the horizontal direction vertical to the door opening and closing.

In an embodiment of the present invention, in step S1, the obtained acceleration data is three-axis acceleration, including acceleration data in three directions of an X axis, a Y axis, and a Z axis. Of course, the direction of acquiring the acceleration data may be set as needed, for example, only the acceleration data in the X-axis direction (corresponding to the upward and downward directions of the elevator), only the acceleration data in the Y-axis direction (corresponding to the left and right directions of the elevator), and only the acceleration in the Z-axis direction (corresponding to the front and rear directions of the elevator) may be acquired; acceleration data in at least two directions of the Z-axis direction, the X-axis direction, and the Y-axis direction may also be acquired.

In an embodiment of the invention, in step S1, the acquired acceleration data includes first-axis direction acceleration data; in step S2, it is determined whether there is a risk in the operation of the device in the first axis direction according to the first axis direction acceleration data of the corresponding device.

In an embodiment of the present invention, in step S1, the filtered acceleration data is uploaded to a cloud database; in step S2, the acceleration data of each elevator is processed by the cloud software, and whether each elevator has a potential risk of failure is determined. Of course, data processing can also be performed by the elevator side.

In an embodiment of the present invention, the acceleration module collects three-axis data every 5ms, and an average value of data collected every 6 points in the same axis is used as a sampling point, that is, a sampling frequency of each axis is 30 ms/point. Before the data is uploaded to the cloud end from the single chip microcomputer, the validity of the data needs to be judged, namely whether the data carries information of elevator motion or not, and the data is discarded when the elevator keeps static. Every time 100 sampling points of three axes are acquired (three axes are acquired simultaneously), the sampling points are taken as a packet and are distributed to a packet I D (each packet I D is increased by 1), and the mean square deviations of three-axis data are calculated respectively. If the mean square deviation value of the data of any axis exceeds the set threshold (100), the data is considered to be valid and needs to be reserved and uploaded to the cloud, and the package I D uploaded each time the condition is met is recorded as n (the value is updated each time). If the condition is not met, the difference between the current packet I D and n is obtained, if the difference is less than 18, uploading is carried out, and if the difference is greater, discarding is carried out. According to the method, the uploading of data can be stopped after the elevator stops running for 54s, so that the data volume uploaded to the cloud end by the lower computer and the storage volume required by the cloud end are reduced. Data received at the cloud end is firstly filtered by a first-order low-pass filter of Butterworth, and the cut-off frequency is set as 1/15 sampling frequency. And smoothing the acceleration sampling data.

In an embodiment of the present invention, the step S21 specifically includes:

In an embodiment of the present invention, in step S212, a condition of a front-rear acceleration rate of the first plurality of acceleration signals is obtained, and a point where the front acceleration signal of the first plurality of acceleration signals starts to change is obtained as a starting point; and acquiring a point of the acceleration signal ending change after the first batch of acceleration signals, and taking the point as a termination point.

In an embodiment of the present invention, the step S21 specifically includes:

step S212, if at least a set point (in an embodiment of the present invention, at least 10 points may be set) appears continuously between the first standard line and the second standard line, a peak or a trough is considered to appear there, and the first point and the last point of the continuous segment are denoted as a and B;

step S213, obtaining corresponding slopes kA and kB at A, B, obtaining d by using the similarity kA ═ data [ a ] -x1)/d and kB ═ x1-data [ B ])/d, and obtaining that the peak or trough should start from the first point before a and end at the second point after B; wherein data [ A ] represents the data value of point A, and d represents the number of points that are a distance from A, B; kA. kB is obtained by dividing the difference between the forward or backward ith point value of the point A and the point B and the point A and the point B by i, wherein i is a set value; in one embodiment of the present invention, i is 5.

Step S214, after all wave crests and wave troughs are obtained, if two adjacent wave crests in the same direction appear, the two wave crests are determined to belong to two single uplink and downlink operation curves; for the peak and trough at both ends of the stationary data segment whose length exceeds the set threshold (in an embodiment of the present invention, the set threshold may be 1500, but may also be other thresholds, such as 500, 2000, 3000, etc.), it is determined that the two single uplink and downlink operations belong to; the peaks of one consecutive data segment are combined with the next following one of the valleys. The stationary data segment refers to a segment of data in which the absolute value of the acceleration is lower than a set value, and at this time, the acceleration of the device approaches zero.

FIG. 8 is a schematic diagram of uplink region segmentation in an embodiment of the present invention; referring to fig. 8, in an embodiment of the present invention, the step S22 specifically includes:

step S221: calculating the acceleration of each point of a corresponding area (uplink area) in one operation action;

step S222: identifying and calculating all area sections with jerk values smaller than a set value (0.05m/s2) on an acceleration curve time axis corresponding to an operation action, and filtering the start and stop area sections to obtain 3 area sections which are respectively a uniform acceleration section, a uniform speed section and a uniform deceleration section;

step S223: dividing the whole uplink region into 7 segments according to the region segment obtained in the step S222, and sequentially forming an acceleration segment, a uniform acceleration segment, a deceleration segment, a uniform speed segment, an acceleration and deceleration segment, a uniform deceleration segment and a deceleration and deceleration segment; the acceleration increasing section is an acceleration time period in which the acceleration is increased, the acceleration decreasing section is an acceleration time period in which the acceleration is decreased, and the uniform acceleration section is an acceleration time period in which the acceleration is not changed; the acceleration and deceleration section is a deceleration time interval with increased acceleration, and the deceleration and deceleration section is a deceleration time interval with decreased acceleration.

In FIG. 8, the marked points are all the regions where the jerk value is less than 0.05m/s2, and FIG. 7 and FIG. 8 are data curves after low pass filtering.

In an embodiment of the present invention, in step S22, it is determined whether the uplink or downlink mode of the device is the uplink or downlink mode; if the initial change direction of the acceleration signal of the one-time operation is the same as the acceleration direction when the equipment is static, the equipment is judged to be in an uplink state, otherwise, the equipment is judged to be in a downlink state.

In an embodiment of the present invention, in step S2, whether each device has a hidden trouble of generating a fault is determined according to an acceleration curve corresponding to acceleration data of the corresponding device; the step S2 specifically includes:

In an embodiment of the present invention, in step S21, one interception manner of the operation action is: if the acceleration signal starts to change (in an embodiment of the present invention, the change amplitude needs to reach a set threshold to start interception, so that filtering is not a slight change of the operation action), the action of intercepting the acceleration signal is started, and the change is ended within the acceleration set time, so that the interception action is ended. The acceleration data acquired during this period is acceleration data of one running motion.

In an embodiment of the present invention, each operation action of the device may be respectively and sequentially intercepted, and each operation action of the device may be respectively determined.

In an embodiment of the invention, in step S21, the acceleration data includes acceleration data of a plurality of discrete data points, and the acceleration data of the plurality of discrete data points is fitted to an acceleration curve; acquiring a peak curve and a trough curve according to the acceleration curve; one peak curve and the corresponding trough curve are determined as an action unit, and at least one action unit is taken as an operation action.

In one embodiment of the invention, the method is used for detecting the operating condition of the elevator equipment; due to the problems of elevator signals and the like, the data of the single chip microcomputer can not be completely uploaded to the cloud due to the phenomenon of packet loss sometimes. The cloud therefore generates many discrete segments of data, namely packet I D (the identification of the packet) discontinuities. In addition, the packet I D may also be discontinued due to the preliminary filtering of step S1. Therefore, after the continuous data segments are found out in the cloud, data processing and judgment are respectively carried out on each data segment, and results generated by each segment of data are summarized.

In an embodiment of the present invention, in step S1, the acquired acceleration data includes acceleration data in a Z-axis direction (corresponding to a direction in which the elevator runs up and down, which may be a Y-axis direction or an X-axis direction of the acceleration sensor, or a Z-axis direction of the acceleration sensor); in step S2, it is determined whether there is a risk in the up-down operation of the elevator based on the Z-axis direction acceleration data of the corresponding elevator.

In an embodiment of the invention, in step S21, a peak curve and a trough curve are obtained according to the acceleration curve; one peak curve and the corresponding trough curve are determined as an action unit, and at least one action unit is taken as an operation action.

In an embodiment of the present invention, in step S21, the smoothed upward single acceleration curve of the elevator is shown in fig. 2, which shows the process of the elevator accelerating from a standstill to the positive X-axis direction, decelerating at a constant speed to the positive X-axis direction. The single acceleration curve for the downward run approximates the curve flipped up and down from fig. 2. In order to find the uplink and downlink single-time operation curves in the continuous data segment, the upward or downward wave peak needs to be found, and then the two rules are utilized to combine the wave peaks. Taking an upward peak as an example, the median x1 of the data segment is first found as the standard line 3 in FIG. 2, x1+20LSB is line 2, and x1+160LSB is standard line 1. If there are more than 10 consecutive points between the standard lines 1 and 2, an upward wave is considered to occur there, noting that the first point of this consecutive segment is a and the last point is B. Then, the slope kA and kB is obtained for the points a and B, and the similarity kA ═ data [ a ] -x1)/d and kB ═ x1-data [ B ])/d are used to obtain d, so that the peak should start from the point a before the first point and end at the point B after the second point. Where data [ A ] represents the data value for point A and d represents the number of points a distance from A, B. kA, kB can be obtained by dividing the difference between the value of the 5 th point forward or backward from the point a, B and the point a, B by 5. After all wave crests are found out in the mode, two adjacent wave crests in the same direction are determined to belong to two single uplink and downlink operation curves according to the rule, two end wave crests of a stable data section with the number of points larger than 1500 are determined to belong to two single uplink and downlink operation curves, and wave crests of a continuous data section are combined in pairs to identify most data sections. But there is also a data error in the continuous data segment, such as considering that two peaks of a segment of the uplink and downlink curve are in the same direction. In this case, the first peak is discarded as default data, and the second peak is combined with the following peak.

In an embodiment of the invention, in step S1, the acquired acceleration data includes second axial direction acceleration data; in step S2, it is determined whether there is a hidden danger in the door opening and closing operation process of the device (e.g., an elevator device, or other devices that need to open or close the door) according to the second axial acceleration data of the corresponding device.

FIG. 7 is a schematic sectional view of a door opening area according to an embodiment of the present invention; referring to fig. 7, in an embodiment of the present invention, step S22 specifically includes:

step S221: calculating the acceleration of each point in the area (door opening area);

step S222: all the segments of which the calculated jerk value is less than the set value (0.05m/s2) are identified on the time axis, resulting in a number of segments including: the system comprises a gantry crane starting area, a plurality of acceleration change interval areas and a gantry crane stopping area; the acceleration change interval areas at least comprise a first acceleration change interval area and a second acceleration change interval area, and intersection points are formed between curves between the first acceleration change interval area and the second acceleration change interval area and an acceleration median line;

In an embodiment of the present invention, as shown in fig. 7, the marked sections are the door driving start area, the first speed changing area, the second speed changing area, the third speed changing area (peak), the fourth speed changing area (valley), the fifth speed changing area, the sixth speed changing area, and the door driving stop area, respectively. The middle point is the intersection point of the curve between the wave crest and the wave trough and the median line.

In an embodiment of the invention, in step S22, the acceleration data includes acceleration data of a plurality of discrete data points, and the acceleration data of the plurality of discrete data points is fitted to an acceleration curve; acquiring the curve slope of each key point in the acceleration curve and the acceleration value of each key point; if the positive and negative values of the slope of the curve change or the positive and negative values of the acceleration value change, the changed points are used as segmentation nodes; wherein, the change of the positive value and the negative value comprises any change among a positive value, zero and a negative value.

In an embodiment of the present invention, step S21 specifically includes:

step S211: finding out wave crests and wave troughs in all acceleration signal curves of the second shaft, finding out wave crests and wave troughs of which the distance between the wave crests and the wave troughs is less than a set number of sampling points, taking the corresponding wave crests and wave troughs and acceleration signals between the wave crests and the wave troughs as partial data in a one-time door opening or closing combination, and taking the partial data as a first batch of acceleration signals; the combination of the door opening is wave crest-wave trough or wave trough-wave crest, and the combination of the door closing is wave trough-wave crest or wave crest-wave trough. The meaning of the wave crest-wave trough is that one wave crest in an acceleration signal curve is connected with one wave trough to form the wave crest-wave trough; the wave trough-wave peak means that one wave trough in an acceleration signal curve is connected with one wave peak to form the wave trough-wave peak.

Step S212: calculating a median y of signal values of the first group of acceleration signals, taking the median y as a third standard line, making a first standard line with the value of y + c parallel to a time axis, a second standard line with the value of y + d, a fourth standard line with the value of y-d, and a fifth standard line with the value of y-c, wherein the first standard line and the fifth standard line are used for filtering extreme points; determining the starting point of the peak through a second standard line, taking N points forward as the starting point of the door opening, determining the ending point of the trough through a fourth standard line, and taking M points backward as the ending point of the door closing; the area from the door opening starting point to the door closing end point is a temporary door opening and closing area; the N points comprise signal values in a short time before the door is opened, and the M points comprise signal points in a short time after the door is closed;

In an embodiment of the present invention, the operation indicated in step S22 may be an ascending or descending operation, or may be a door opening or/and closing operation; can be freely set according to requirements. In some embodiments of the present invention, an operation may be an ascending operation, a descending operation, or a combination of opening and closing the door.

In an embodiment of the present invention, in step S22, a curve slope of each key point in the acceleration curve and an acceleration value of each key point are obtained; if the positive and negative values of the slope of the curve change or the positive and negative values of the acceleration value change, the changed points are used as segmentation nodes; wherein, the change of the positive value and the negative value comprises any change among a positive value, zero and a negative value.

Fig. 5 is a schematic diagram of speed and acceleration curves in the up-down process of the elevator in one embodiment of the invention; referring to fig. 5, in one embodiment of the present invention, the speed and acceleration of the elevator during the up and down movement are shown in fig. 5.

In an embodiment of the present invention, in step S1, the acquired acceleration data includes acceleration data in an X-axis direction (corresponding to a door opening and closing direction of the elevator, which may be a Y-axis or a Z-axis direction of the acceleration sensor, or may be an X-axis direction of the acceleration sensor); in step S2, it is determined whether there is a risk in the door opening and closing operation of the elevator based on the X-axis acceleration data of the corresponding elevator.

In one embodiment of the present invention, the identification of the elevator door opening and closing in step S21 is similar to the up and down movement, but differs. The waveform of the opening and closing door of the elevator is not divided upwards and downwards, the curve trend is basically consistent, but the sensor can deflect to a certain degree before and after the opening of the elevator door, so that the waveform can change. Referring to fig. 4, the median y1 of the curve of the opening and closing door of the elevator is found, the 4 intercepted standard lines are y1+400, y1+10, y1-10 and y1-400 respectively, and the continuous curve in the region is intercepted when the continuous points are 5 points or more within the standard line 1 and the standard line 2 or within the standard line 4 and the standard line 5. Then, finding a peak combination to form a one-time complete door opening and closing curve, and concretely implementing as follows: first, it is determined whether the door opening/closing curve starts from an upward peak or a downward valley. Because the curve of the opening and closing door is greatly interfered by the ascending and descending (the acceleration sensor is relatively deviated to a certain degree with the elevator, and the Y axis is influenced by the acceleration sensor to generate fluctuation when the elevator ascends and descends), the starting wave crest direction is directly determined by the opening and closing door in a section of non-ascending and descending section, and the starting wave crest direction is accidental, so that statistics is carried out by the trend of the first wave crest in a plurality of sections of non-ascending and descending sections, and the frequency is more than that of the actual opening and closing door. Taking fig. 4 as an example, the sequence of the truncated peaks is, at the beginning, an upward peak, a downward valley (in the present method, the whole section within the standard line 4 and the standard line 5 is a valley), and an upward peak. Therefore, after all the peaks and troughs are cut from a segment of continuous packet id data, the direction of every two adjacent peaks and troughs is upward and downward, which is the starting end, and downward and upward, which is the ending end. Two starting ends and two ending ends which are nearest form a section of door opening and closing curve, and two rules are different: the distance between two opposite wave crests (such as point A to point B and point C to point D in the figure 4) is not more than 100 points, and the distance between two homodromous waves is not more than 4000 points (such as point A to point D in the figure 4); the abnormal data is discarded.

The above steps result in a complete door opening and closing curve, and the door opening and closing needs to be divided into specific door opening and closing curves. Also taking fig. 4 as an example, several curves like fig. 3 can be obtained after the above steps. Because the elevator door is re-opened, the temporary door opening and closing area obtained by the temporary door opening and closing area obtaining subunit is not necessarily a complete one-time door opening and complete closing process, and the real elevator door opening and closing area obtaining subunit combines all the temporary door opening and closing areas in a superposition mode or at too close interval time to obtain a real elevator door opening and closing area.

The above steps have roughly intercepted all the curves of opening and closing the door of the elevator, and then the curves need to be accurately divided on the basis of the curves. The judgment principle is as follows: the data returned by the acceleration sensor under the static condition of the elevator is a regular oscillation curve and tends to be stable after low-pass filtering, and in the condition, if the difference between continuous multi-point adjacent data points is larger than a threshold value, namely the data is not stable any more, the elevator door is considered to be in a motion state. In one embodiment of the invention, the difference value of the acceleration of every two adjacent points of 10 continuous points is calculated, if 1 difference value exceeds a threshold value of 0.1, the elevator is considered to be in a running state (because of the short-time constant speed and the variation of the acceleration direction in the process of opening and closing the door of the elevator, a few cases that the acceleration value is smaller than the threshold value may occur). Since the oscillation of the curve cannot be completely eliminated, the open-close or close-open curve obtained by the above steps is generally intercepted into several segments of data, and the longest segment (i.e., the segment containing the largest number of data points) is taken as the accurate open (close) door data curve.

In an embodiment of the present invention, in step S22, the door opening curve before the point M in fig. 4 is taken as an example, and is divided into 8 segments according to the variation of the jerk direction. Identifying and calculating all the area sections with the jerk value smaller than the set value on the time axis to obtain a plurality of area sections, including: the system comprises a gantry crane starting area, a plurality of acceleration change interval areas and a gantry crane stopping area; the plurality of jerk change interval areas at least comprise a first jerk change interval area and a second jerk change interval area, and intersection points are formed between curves between the first jerk change interval area and the second jerk change interval area and an acceleration median line. And dividing the whole operating curve into a plurality of sections according to the gantry crane starting area, the gantry crane stopping area, the plurality of jerk change interval areas and the intersection point obtained by the key area section obtaining subunit.

Fig. 6 is a schematic diagram of speed and acceleration curves during the door opening and closing process of an elevator according to an embodiment of the present invention; referring to fig. 6, in an embodiment of the present invention, the speed and acceleration during the door opening and closing process are shown in fig. 6.

In an embodiment of the present invention, in step S23, the maximum jerk, the average amplitude, the time used, and the total maximum speed, the maximum acceleration, the value on three axes returned by the acceleration sensor at rest, etc. of each piece of data are calculated respectively; listing all abnormal parameters of the equipment, and presuming which abnormal expressions and the probability of the abnormal expressions possibly occurring in the equipment according to the relation of the equipment parameters, the fault expressions and the fault reasons, and further presuming which faults or hidden dangers possibly occurring or existing in the equipment.

In one embodiment of the present invention, the present invention is provided with a data table (or other form of data or condition storage) capable of recording: (1) under normal conditions, the maximum jerk, the average amplitude, the time of use, the overall maximum speed, the maximum acceleration, the value on three axes returned by the acceleration sensor when the acceleration sensor is static and other data ranges of each section of data in all directions during the operation of the equipment; (2) in the case of each fault, the maximum jerk, average amplitude, time of use, and overall maximum speed, maximum acceleration, three-axis values returned by the acceleration sensor at rest, etc. for each piece of data in each direction during operation of the device.

In an embodiment of the present invention, acceleration data of each segment of data in each direction in each operation process is compared with data in a data table (or other data or condition storage forms) to determine whether there is a fault or not and whether there is a fault or not in the operation process of the device (for example, a stone appears in a slide way of an elevator door, which can sense shaking information of the elevator door during opening and closing and influence door opening and closing speed, but belongs to a fault hidden danger although it may not be considered as a fault), if it is determined that there is a fault or a fault hidden danger, the type of the fault or the fault hidden danger is further determined, and the fault or the fault hidden danger is sent to a server, and the server sends the server to a related terminal to make necessary reminders.

In an embodiment of the present invention, in step S23, after the up-down and door opening and closing data of the elevator are divided, the data of all the continuous data segments are summarized, and the characteristics of each segment of data, including the maximum jerk, average amplitude, time, and the values on three axes returned by the acceleration sensor at the maximum speed, the maximum acceleration, and the standstill of the whole, are calculated respectively.

The motion units of the first and second axes are sampled separately. Because the running times of each elevator in unit time are different, the sampling is preferably carried out according to the percentage of the running times of the elevators. In this example, 5 samples are taken for every 30 motion units, assuming that each feature is normally distributed. The mean μ and standard deviation s of the calculated features were obtained for 5 samples.

After sampling 25 batches, 25 average values and standard deviations are obtained and then the average value is calculated and recordedAnd respectively calculate two sets of valuesAnd

first, it is determined whether the feature is in a steady state in the 25 samples, i.e., any one of the following exceptions does not occur. If the abnormal condition exists, the abnormal condition of the parameters is observed, and the abnormal condition and the probability of the abnormal condition which may occur to the elevator are presumed according to the relationship graph of the elevator parameters, the fault condition and the fault reason, so that the possible occurrence or the existence of the fault or the hidden danger of the elevator is further presumed. After the fault is eliminated, sampling is performed for 25 times to check whether the characteristic is in a steady state.If the features are all in steady state, sampling is continued, and the mean and standard deviation averages are observed after each samplingWhether abnormal conditions still do not exist after the data is spliced with the previous data or not. If the fault exists, the fault is checked and re-sampled, otherwise, the elevator is considered to be sampled all the time without the fault.

In an embodiment of the present invention, 8 criteria for determining an abnormality are adopted:

(1) obtained by sampling any of iOrOrOr

(2) All of the values of μ or s for 9 consecutive samples are greater or less than their corresponding mean values

(3) μ or s for 6 consecutive samples is incremented or decremented in steps;

(4) comparing the magnitude of mu or s of 14 continuous samples with the magnitude of the adjacent previous value alternately;

(5) 2 of the values of mu or s of 3 consecutive samples are greater thanOrOr 2 values less thanOr

(6) 4 of the values of mu or s of 5 consecutive samples are greater thanOrOr 4 values less thanOr

(7) In mu or s of 15 consecutive samplesOrWithin the range;

(8) mu or s in 8 consecutive samplesOrOutside the range.

In conclusion, the elevator abnormity judgment method provided by the invention can acquire the acceleration data according to the running condition of the elevator, and then can judge whether the elevator runs abnormally (for example, the door opening and closing time is prolonged because the elevator door is blocked by foreign matters) according to the acceleration data, can judge the elevator fault, can find the abnormal condition which possibly causes the elevator fault, and effectively ensures the use safety of the elevator.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The description and applications of the invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. Variations and modifications of the embodiments disclosed herein are possible, and alternative and equivalent various components of the embodiments will be apparent to those skilled in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other components, materials, and parts, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.

Claims

1. An apparatus operation abnormality judgment method is characterized in that the apparatus operation abnormality judgment method comprises:

2. The device operation abnormality judgment method according to claim 1, characterized in that:

the step S2 specifically includes:

3. The device operation abnormality judgment method according to claim 1, characterized in that:

in step S1, uploading the filtered acceleration data to a cloud database; in step S2, the acceleration data of each device is processed by the cloud software, and whether each device has a hidden trouble of generating a fault is determined.

4. The device operation abnormality judgment method according to claim 1, characterized in that:

in step S1, the acquired acceleration data is three-axis acceleration, including acceleration data in three directions, i.e., the first axis, the second axis, and the third axis.

5. The device operation abnormality judgment method according to claim 1, characterized in that:

in step S1, the device is an elevator, and acceleration information of elevator operation is acquired by providing an acceleration sensor in an elevator light curtain.

6. The device operation abnormality judgment method according to claim 1, characterized in that:

in step S1, the acquired acceleration data includes first-axis direction acceleration data;

7. The device operation abnormality judgment method according to claim 6, characterized in that:

in step S1, the acquired acceleration data is three-axis acceleration, including acceleration data in three directions of a first axis, a second axis, and a third axis; the third axis direction is vertical to a plane formed by the first axis direction and the second axis direction;

8. The device operation abnormality judgment method according to claim 6, characterized in that:

in step S21, the acceleration data includes acceleration data of a plurality of discrete data points, and the acceleration data of the plurality of discrete data points is fitted to an acceleration curve;

9. The device operation abnormality judgment method according to claim 6, characterized in that:

the step S21 includes:

10. The device operation abnormality determination method according to claim 9, characterized in that:

in step S212, the condition of the acceleration degree change rate before and after the first batch of acceleration signals is obtained, and the point of the acceleration signals starting to change before the first batch of acceleration signals is obtained and is used as the starting point; and acquiring a point of the acceleration signal ending change after the first batch of acceleration signals, and taking the point as a termination point.

11. The device operation abnormality judgment method according to claim 6, characterized in that:

the step S21 specifically includes:

12. The device operation abnormality judgment method according to claim 6, characterized in that:

the step S22 includes:

13. The apparatus operation abnormality determination method according to claim 12, characterized in that:

in step S22, it is determined whether the device is in an uplink or downlink mode; if the initial change direction of the acceleration signal of the one-time operation is the same as the acceleration direction when the equipment is static, the equipment is judged to be in an uplink state, otherwise, the equipment is judged to be in a downlink state.

14. The device operation abnormality judgment method according to claim 1, characterized in that:

in step S1, the acquired acceleration data includes second axial direction acceleration data;

15. The apparatus operation abnormality determination method according to claim 14, characterized in that:

16. The apparatus operation abnormality determination method according to claim 14, characterized in that:

17. The apparatus operation abnormality determination method according to claim 14, characterized in that:

in step S22, the acceleration data includes acceleration data of a plurality of discrete data points, and the acceleration data of the plurality of discrete data points is fitted to an acceleration curve; acquiring the curve slope of each key point in the acceleration curve and the acceleration value of each key point; if the positive and negative values of the slope of the curve change or the positive and negative values of the acceleration value change, the changed points are used as segmentation nodes; wherein, the change of the positive value and the negative value comprises any change among a positive value, zero and a negative value.

18. The apparatus operation abnormality determination method according to claim 14, characterized in that:

step S21 includes:

19. The apparatus operation abnormality determination method according to claim 14, characterized in that:

step S21 includes:

step S212: calculating a median y of signal values of the first group of acceleration signals, taking the median y as a third standard line, making a first standard line with the value of y + c parallel to a time axis, a second standard line with the value of y + d, a fourth standard line with the value of y-d, and a fifth standard line with the value of y-c, wherein the first standard line and the fifth standard line are used for filtering extreme points; determining the starting point of a wave crest through a second standard line, taking N points forward as a door opening starting point, determining the end point of a wave trough through a fourth standard line, and taking M points backward as a door closing end point; the area from the door opening starting point to the door closing end point is a temporary door opening and closing area; the N points comprise signal values in a short time before the door is opened, and the M points comprise signal points in a short time after the door is closed; wherein c and d are set values;

20. The apparatus operation abnormality determination method according to claim 14, characterized in that:

step S22 includes:

step S221: calculating the acceleration of each point in the area;

21. The device operation abnormality judgment method according to claim 1, characterized in that:

in step S23, the maximum jerk, average amplitude, elapsed time, and overall maximum velocity, maximum acceleration, and three-axis values returned by the acceleration sensor at rest of each piece of data are calculated respectively;