Predictive maintenance method for device by using cluster waveforms

Abstract

Disclosed is a predictive maintenance method for a device by using cluster waveforms, the method comprising: an information collecting step for collecting information about a change in energy size over time, as measured by the device, to collect, in large quantities, energy waveform information for various operations performed by the device; a cluster waveform extracting step for forming a plurality of similar clusters by classifying, according to similar waveforms, a large amount of energy waveforms collected in the information collecting step, and extracting a cluster waveform from each of the plurality of similar clusters; and a detecting step for, when the device performs an operation in real time, measuring and collecting a real-time energy waveform from the device performing the operation, and detecting an anomaly of the device in real time by comparing the collected real-time energy waveform with information about a plurality of cluster waveforms extracted in the cluster waveform extracting step.

Description

Predictive maintenance method for devices using cluster waveforms

[0001] The present invention relates to a predictive maintenance method for a device using a cluster waveform, and more specifically, to a predictive maintenance method for a device using a cluster waveform, which collects a large amount of energy waveform information measured from a device freely performing various operations while performing each operation, classifies the large amount of collected energy waveforms by similar waveforms to form a small number of similar clusters, extracts a small number of cluster waveforms representing each similar cluster based on the similar waveforms belonging to the similar clusters, and then matches and compares real-time energy waveforms measured from a device freely operating in real time with the small number of cluster waveforms to detect an abnormality in the device and issues an alarm to induce management such as replacement or repair before a device malfunctions, thereby preventing economic losses, safety accidents, inconveniences, etc., caused by sudden device malfunctions in advance.

[0002] In general, stable operation is very important for various devices used for automation processes of facilities.

[0003] For example, in the facilities of a large-scale production plant, tens or hundreds of devices are installed and operate in conjunction with each other to continuously produce products. If a single device among the many devices breaks down, a catastrophic situation can occur where the entire operation of the facility comes to a halt.

[0004] At this time, downtime due to equipment failure inevitably leads to enormous losses not only in equipment repair costs but also in operating costs and business effects wasted while the equipment is down.

[0005] To prevent such damage, predictive maintenance methods are being developed to detect and respond to abnormal signs in devices in advance and prevent damage caused by sudden device failures. However, these predictive maintenance methods have the problem that they are difficult to apply to devices that perform irregular and diverse operations because they detect abnormal signs in devices that perform repetitive and regular operations.

[0006] Therefore, for devices that freely (irregularly) perform various movements, such as elevators and cranes, it is still somewhat difficult to detect abnormal signs in advance, and conventional problems caused by sudden device failures still occur.

[0007] In particular, sudden breakdowns of equipment such as elevators can lead to safety accidents that can result in casualties or cause inconvenience to users.

[0008] The present invention has been proposed to solve the above-mentioned problems, and its purpose is to provide a predictive maintenance method for a device using a cluster waveform, which collects a large amount of energy waveform information measured by the device when performing each operation from a device that freely performs various operations, classifies the collected large amount of energy waveforms by similar waveforms to form a small number of similar clusters, extracts a small number of cluster waveforms representing each similar cluster based on the similar waveforms belonging to the similar clusters, and then matches the real-time energy waveform measured from the device that freely operates in real time with the small number of cluster waveforms to detect an abnormality in the device, thereby generating an alarm and inducing management such as replacement or repair in advance before a device malfunctions, thereby preventing economic losses, safety accidents, inconveniences, etc., caused by sudden device malfunctions.

[0009] In order to achieve the above object, the predictive maintenance method of a device using a cluster waveform according to the present invention is characterized by including an information collection step (S10) of collecting information on changes in energy size over time measured by a device performing each operation in a device performing various operations, thereby collecting a large amount of energy waveform information for various operations performed by the device; a cluster waveform extraction step (S20) of classifying a large amount of energy waveforms collected in the information collection step (S10) by similar waveforms to form a plurality of similar clusters, and extracting cluster waveforms from each of the plurality of similar clusters; and a detection step (S30) of measuring and collecting real-time energy waveforms from a device performing an operation in real time, when the device performs the operation, and comparing the collected real-time energy waveform with a plurality of cluster waveform information extracted in the cluster waveform extraction step (S20) to detect abnormal signs of the device in real time.

[0010] In addition, the cluster waveform extraction step (S20) is characterized by including a classification step (S21) of classifying and collecting a large amount of energy waveforms collected in the information collection step (S10) by similar waveforms to form a plurality of similar clusters, a peak extraction step (S22) of measuring and collecting peak values ​​for energy waveforms for each similar cluster classified in the classification step (S21) by taking the largest energy value in the energy waveforms collected in the information collection step (S10) as a peak value, and a waveform extraction step (S23) of forming and extracting cluster waveforms having a predetermined thickness for each similar cluster by overlapping energy waveforms belonging to similar clusters in the classification step (S21).

[0011] In addition, the detection step (S30) is characterized by including a measurement process (S31) of collecting information on changes in energy size over time measured from a device performing real-time operation, measuring the energy waveform of the device for real-time operation, and measuring and collecting peak values ​​from the measured energy waveform, a search process (S32) of searching and matching a waveform having a similar shape to the energy waveform of the device for real-time operation measured in the measurement process (S31) among a plurality of cluster waveforms, and a judgment process (S33) of setting threshold values ​​for peak values ​​of cluster waveforms extracted for each similar cluster in the waveform extraction process (S23), and determining the device as unstable and giving an alarm when the peak value of the real-time energy waveform of the device in the search process (S32) exceeds the threshold value for the searched and matched cluster waveform.

[0012] In addition, if a waveform similar to the real-time energy waveform of the device is not searched and matched among a plurality of cluster waveforms in the search process (S32) of the detection step (S30), the judgment process (S33) is characterized in that the real-time device status is judged to be unstable and an alarm is issued.

[0013] In addition, the energy measured and collected from the device is characterized in that any one of the following is selectively used: current consumed in operating the device, vibration generated when operating the device, noise generated when operating the device, frequency of power supplied to the device, temperature, humidity, and pressure of the device when operating the device.

[0014] As described above, according to the predictive maintenance method of a device using a cluster waveform according to the present invention, a large amount of energy waveform information measured by the device while performing each operation is collected from a device that freely performs various operations, the collected large amount of energy waveforms are classified by similar waveforms to form a small number of similar clusters, and a small number of cluster waveforms representing each similar cluster are extracted based on the similar waveforms belonging to the similar clusters, and then the real-time energy waveform measured from the device that freely operates in real time is matched and compared with the small number of cluster waveforms to detect an abnormality in the device, thereby generating an alarm and inducing management such as replacement or repair in advance before a device failure occurs, thereby preventing in advance economic losses, safety accidents, inconveniences, etc. due to sudden device failure.

[0015] FIG. 1 is a block diagram of a method for predictive maintenance of a device using a cluster waveform according to an embodiment of the present invention.

[0016] FIG. 2 to FIG. 6 are drawings for explaining a predictive maintenance method of a device using the cluster waveform shown in FIG. 1.

[0017] A method for predictive maintenance of a device using cluster waveforms according to a preferred embodiment of the present invention is described in detail with reference to the attached drawings. Detailed descriptions of known functions and configurations that may unnecessarily obscure the gist of the present invention are omitted.

[0018] FIGS. 1 to 6 illustrate a predictive maintenance method for a device using a cluster waveform according to an embodiment of the present invention. FIG. 1 is a block diagram of a predictive maintenance method for a device using a cluster waveform according to an embodiment of the present invention, and FIGS. 2 to 6 are drawings for explaining the predictive maintenance method for a device using the cluster waveform illustrated in FIG. 1, respectively.

[0019] As illustrated in Fig. 1, the predictive maintenance method (100) of a device using a cluster waveform according to an embodiment of the present invention includes an information collection step (S10), a cluster waveform extraction step (S20), and a detection step (S30).

[0020] The above information collection step (S10) is a step for collecting a large amount of energy waveform information for various operations performed by a device by collecting information on changes in energy size over time measured by the device while performing each operation.

[0021] Here, in the information collection step (S10), the energy measured and collected from the device performing various operations may be any one of the current consumed in operating the device, the vibration generated when operating the device, the noise generated when operating the device, the frequency of the power supplied to the device, the temperature, humidity, pressure, etc. of the device when operating the device. In the predictive maintenance method (100) of the device using a cluster waveform of the present invention, the current consumed in operating the device is selectively used as the energy measured from the device, but the selection and use is not limited to this current.

[0022] That is, when information on changes in the size (value) of current consumed by a device performing an operation in the above information collection step (S10) is collected over time, a current (energy) waveform such as a graph is collected. Since the device performs various operations, various types of current waveforms are collected.

[0023] Here, there are devices such as elevators and cranes that freely (irregularly) perform various movements. For convenience of explanation, in the present invention, the above device is set as an elevator that is installed in a 10-story building and can accommodate about 10 people, and in the information collection step (S10), current consumed by the elevator was measured during the elevator's various movements for 30 days to collect various types of current waveforms.

[0024] As a result of measuring and collecting current waveforms from the elevator for 30 days, approximately 8,500 current waveforms were collected.

[0025] Since the predictive maintenance method (100) of the device using the cluster waveform of the present invention detects abnormal signs of the device freely performing various operations, it is desirable to induce sufficient acquisition of current waveform information on various operations performed by the device. As described above, the current waveform was measured and collected from the elevator for a total of 30 days. However, it is of course possible to freely set the period for collecting current waveform information by considering the type of device, the surrounding environment in which the device is used, and other conditions.

[0026] Here, the various types of current waveform information collected from the device are used as a medium for extracting a small number of cluster waveforms that serve as a basis for detecting abnormal symptoms of the device in the cluster waveform extraction step (S20) to be described later. The current waveform information collected in the information collection step (S10) is assumed to have been measured and collected in an elevator operating in a normal state.

[0027] The above cluster waveform extraction step (S20) classifies a large amount of energy waveforms collected in the above information collection step (S10) by similar waveforms to form a plurality of similar clusters, and extracts cluster waveforms from each of the plurality of similar clusters, and is comprised of a classification process (S21), a peak extraction process (S22), and a waveform extraction process (S23).

[0028] The above classification process (S21) is a process of classifying and collecting a large amount of energy waveforms collected in the above information collection step (S10) by similar waveforms to form a number of similar clusters.

[0029] That is, in the information collection step (S10), a large number of current waveforms are collected and classified by similar waveforms to form a small number of clusters. As described above, about 8,500 current waveforms collected from the elevator are classified by similar waveforms, and a total of 91 similar clusters are classified, and these 91 similar clusters represent the entire current waveforms (about 8,500 current waveforms).

[0030] Figure 2 illustrates some of the few similar clusters classified by similar waveforms. When a total of 91 similar clusters are sequentially numbered as the 1st similar cluster, the 2nd similar cluster, …, the 91st similar cluster, the waveforms classified as the 20th similar cluster to the 40th similar cluster are shown.

[0031] The above peak extraction process (S22) is a process of taking the largest energy value in the energy waveform collected in the information collection step (S10) as the peak value, and measuring and collecting the peak value for the energy waveform for each similar cluster classified in the classification process (S21).

[0032] Here, as illustrated in FIG. 3, the peak values ​​for the energy waveforms being measured and collected are collected separately for each similar cluster. The reason for this is to set a threshold value for the peak value of the cluster waveform extracted from each similar cluster based on the peak value of the cluster waveform collected separately for each similar cluster. This will be described in detail in the detection step (S30) below.

[0033] The above waveform extraction process (S23) is a process of forming and extracting a cluster waveform having a predetermined thickness for each similar cluster by overlapping energy waveforms belonging to similar clusters in the above classification process (S21).

[0034] That is, when energy waveforms belonging to similar clusters are superimposed, a single cluster waveform with a predetermined thickness is formed. In the classification process (S21), approximately 8,500 current waveforms collected from the elevator are classified into a total of 91 similar clusters, so a total of 91 cluster waveforms extracted from a total of 91 similar clusters will be formed as shown in FIG. 4, and the 91 cluster waveforms formed in this way have representativeness for the entire current waveform.

[0035] Here, among the current waveforms of the above similar clusters, current waveforms with an abnormal shape (noise cluster) are removed, and similar clusters are formed based on meaningful current waveforms, and cluster waveforms are extracted.

[0036] In addition, a cluster waveform having a predetermined thickness is extracted by overlapping energy waveforms belonging to the above-mentioned similar clusters, but a line connecting the center, bottom, or top of the waveform thickness formed by overlapping energy waveforms can also be formed and extracted as a cluster waveform.

[0037] As illustrated in FIG. 5, the detection step (S30) measures and collects real-time energy waveforms from the device performing the operation when the device performs the operation in real time, and compares the collected real-time energy waveforms with a plurality of cluster waveform information extracted in the cluster waveform extraction step (S20) to detect abnormal signs of the device in real time, and is composed of a measurement process (S31), a search process (S32), and a judgment process (S33).

[0038] The above measurement process (S31) is a process of collecting information on changes in energy size over time measured from a device performing real-time operation, measuring the energy waveform of the device for real-time operation, and measuring and collecting peak values ​​from the measured energy waveform.

[0039] That is, when the elevator operates to detect the status of the elevator in real time, the current waveform measured from the elevator during the operation is measured and collected in real time.

[0040] The above search process (S32) is a process of searching and matching a waveform having a shape similar to the energy waveform of the device for real-time operation measured in the above measurement process (S31) among a plurality of cluster waveforms.

[0041] That is, it is a process of searching and matching cluster waveforms similar to real-time current waveforms by matching real-time current waveforms measured from an elevator with a total of 91 cluster waveforms extracted in the cluster waveform extraction step (S20).

[0042] The above judgment process (S33) is a process of setting a threshold value for the peak value of the cluster waveform extracted for each similar cluster in the waveform extraction process (S23), and, if the peak value of the real-time energy waveform of the device in the search process (S32) exceeds the threshold value for the searched matched cluster waveform, determining the device to be in an unstable state and issuing an alarm.

[0043] Here, the peak value refers to the current value at which the current reaches its maximum size at a point in time when the device requires the use of high current to perform an operation. Since this peak value is formed at various sizes (values) and points in time depending on each operation, the cluster waveforms extracted for each similar cluster are collected separately, and the threshold for the peak value is set separately for each cluster waveform.

[0044] That is, in the above judgment process (S33), if the peak value of the real-time current waveform of the elevator measured in the above measurement process (S31) is formed below the threshold value set in the cluster waveform matched in the above search process (S32), the elevator is judged to be in a stable state.

[0045] Conversely, if the peak value of the real-time current waveform of the elevator measured in the above measurement process (S31) exceeds the threshold value set in the cluster waveform matched in the above search process (S32), the elevator is judged to be in a somewhat unstable state and an alarm is generated so that management such as inspection or repair can be performed in advance before a breakdown occurs in the elevator.

[0046] Here, the threshold value for the peak value of each cluster waveform set in the above judgment process (S33) can be set to values ​​of various sizes in consideration of conditions such as the type of device, usage environment, and lifespan, and the threshold value can be divided into at least two threshold values, for example, an alarm threshold value and a danger threshold value at a level higher than the alarm threshold value, and the level of the alarm can be formed in various ways to alert for abnormal signs of the device in stages.

[0047] Of course, if the elevator freely performs various operations, the current waveform for each operation can be measured in real time from the elevator, and the status of the elevator performing each operation can be continuously detected and judged in the same manner as above, so that abnormal signs of irregularly operating equipment can be easily detected in real time and effectively preventive maintenance can be performed.

[0048] Meanwhile, as illustrated in FIG. 6, if a waveform similar to the real-time energy waveform of the device is not searched and matched among a plurality of cluster waveforms in the search process (S32) of the detection step (S30), an alarm is issued in the judgment process (S33) to judge the real-time device status as unstable.

[0049] In other words, if a waveform similar to the real-time current waveform of the elevator does not exist in a small number of cluster waveforms (a total of 91 cluster waveforms), it means that the real-time current waveform of the elevator is a waveform of a different form from the current waveform measured in a normal elevator, and such a current waveform is sufficient reason to suspect the status of the elevator, so an alarm is issued to judge the elevator to be in a somewhat unstable state so that management such as inspection or repair can be performed in advance before a breakdown occurs in the elevator.

[0050] The predictive maintenance method (100) of the present invention using a cluster waveform, which is comprised of the above-described process, collects a large amount of energy waveform information measured from a device performing each operation from a device that freely performs various operations, classifies the collected large amount of energy waveforms by similar waveforms to form a small number of similar clusters, extracts a small number of cluster waveforms representing each similar cluster based on the similar waveforms belonging to the similar clusters, and then matches and compares the real-time energy waveform measured from the device that freely operates in real time with the small number of cluster waveforms to detect an abnormality in the device, thereby generating an alarm and inducing management such as replacement or repair in advance before a device malfunctions, thereby preventing economic losses, safety accidents, inconveniences, etc., caused by sudden device malfunctions in advance.

[0051] While the present invention has been described with reference to the embodiments illustrated in the accompanying drawings, these are merely illustrative and are not limited to the above-described embodiments. Those skilled in the art will readily appreciate that various modifications and equivalent embodiments are possible. Furthermore, it should be understood that modifications are possible by those skilled in the art without departing from the spirit and scope of the present invention. Therefore, the scope of the claims in the present invention is not limited to the detailed description, but rather by the claims set forth below and their technical spirit.

[0052] The present invention can be used in the predictive maintenance industry of equipment.

Claims

1. In a predictive maintenance method for a device that performs various movements irregularly, An information collection step (S10) that collects information on changes in energy size over time measured by the device and collects a large amount of energy waveform information on various operations performed by the device; A cluster waveform extraction step (S20) that classifies a large amount of energy waveforms collected in the above information collection step (S10) into similar waveforms to form a plurality of similar clusters, and extracts cluster waveforms from each of the plurality of similar clusters; and A predictive maintenance method for a device using a cluster waveform, characterized in that it comprises a detection step (S30) for measuring and collecting real-time energy waveforms from a device performing an operation in real time, and detecting abnormal signs of the device in real time by comparing the collected real-time energy waveforms with a plurality of cluster waveform information extracted in the cluster waveform extraction step (S20).

2. In paragraph 1, The above cluster waveform extraction step (S20) is A classification process (S21) that collects a large amount of energy waveforms collected in the above information collection step (S10) and classifies them into similar waveforms to form a number of similar clusters, The largest energy value in the energy waveform collected in the above information collection step (S10) is taken as the peak value, and a peak extraction process (S22) is performed to measure and collect the peak value for the energy waveform for each similar cluster classified in the above classification process (S21). A predictive maintenance method for a device using a cluster waveform, characterized in that it includes a waveform extraction process (S23) for forming and extracting a cluster waveform having a predetermined thickness for each similar cluster by overlapping energy waveforms belonging to similar clusters in the above classification process (S21).

3. In paragraph 2, The above detection step (S30) is A measurement process (S31) that collects information on changes in energy size over time measured from a device performing real-time operation, measures the energy waveform of the device for real-time operation, and measures and collects peak values ​​from the measured energy waveform. A search process (S32) for searching and matching a waveform having a shape similar to the energy waveform of the device for real-time operation measured in the above measurement process (S31) among a plurality of cluster waveforms, A predictive maintenance method for a device using a cluster waveform, characterized in that the method comprises a judgment process (S33) for determining that the device is in an unstable state and providing an alarm when the peak value of the cluster waveform extracted for each similar cluster is set for each similar cluster in the waveform extraction process (S23) and the peak value of the real-time energy waveform of the device in the search process (S32) exceeds the threshold value for the searched matched cluster waveform.

4. In paragraph 3, A predictive maintenance method for a device using a cluster waveform, characterized in that if a waveform similar to the real-time energy waveform of the device is not searched and matched among a plurality of cluster waveforms in the search process (S32) of the above detection step (S30), an alarm is issued in the judgment process (S33) judging the real-time status of the device as unstable.

5. In paragraph 1, A predictive maintenance method for a device using a cluster waveform, characterized in that the energy measured and collected from the device is selected from among the current consumed in operating the device, vibration generated when operating the device, noise generated when operating the device, frequency of power supplied to the device, temperature, humidity, and pressure of the device when operating the device.

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