Method for predictive maintenance of device through group waveform

Abstract

Disclosed is a method for predictive maintenance of a device through a group waveform, the method comprising: an information collecting step of collecting information regarding a change in energy magnitude over time, measured in a device, thereby collecting a large amount of information regarding energy waveforms regarding various operations performed by the device; a group waveform extracting step of classifying the large amount of energy waveforms collected in the information collecting step with regard to similar waveforms, thereby forming multiple similar groups, and extracting respective group waveforms from the multiple similar groups; and a detecting step in which, if the device performs a real-time operation, real-time energy waveforms are measured / collected from the device performing the operation, and the collected real-time energy waveforms and the information regarding multiple group waveforms extracted in the group waveform extracting step are compared, thereby detecting abnormal symptoms of the real-time device.

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 particularly, to a predictive maintenance method for a device using a cluster waveform, which collects a large amount of energy waveform information measured by the device while 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 real-time energy waveforms measured from the device that freely operates 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 facility's operation 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 process (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, and an extraction process (S22) 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 process (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 and measuring an energy waveform of the device for real-time operation, a search process (S32) of searching for a waveform having a shape similar 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 detecting the device as being in a stable state if a cluster waveform similar to the real-time energy waveform of the device is matched through the search process (S32), and detecting the device as being in an unstable state if a cluster waveform similar to the real-time energy waveform of the device is not matched.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] FIG. 2 to FIG. 5 are drawings for explaining a predictive maintenance method of a device using the cluster waveform shown in FIG. 1.

[0016] 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.

[0017] FIGS. 1 to 5 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 5 are drawings for explaining a predictive maintenance method for a device using a cluster waveform illustrated in FIG. 1.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] As a result of measuring and collecting current waveforms from the elevator for 30 days, approximately 8,500 current waveforms were collected.

[0024] 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.

[0025] 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.

[0026] 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) and an extraction process (S22).

[0027] 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.

[0028] 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).

[0029] 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.

[0030] The above extraction process (S22) 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).

[0031] 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. 3, and the 91 cluster waveforms formed in this way have representativeness for the entire current waveform.

[0032] 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.

[0033] 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.

[0034] The above 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. The detection step (S30) consists of a measurement process (S31), a search process (S32), and a judgment process (S33).

[0035] 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 and measuring the energy waveform of the device for real-time operation.

[0036] 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.

[0037] The above search process (S32) is a process of searching among a plurality of cluster waveforms for a waveform having a shape similar to the energy waveform of the device for real-time operation measured in the above measurement process (S31).

[0038] 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).

[0039] The above judgment process (S33) is a process of detecting the device as being in a stable state if a cluster waveform similar to the real-time energy waveform of the device is matched through the above search process (S32), and detecting the device as being in an unstable state if a cluster waveform similar to the real-time energy waveform of the device is not matched.

[0040] That is, as shown in Fig. 4, if the real-time current waveform measured from the elevator matches one of the cluster waveforms among a small number of cluster waveforms, it means that the real-time current waveform corresponds to a current waveform that can be measured in a normal elevator, and therefore the real-time elevator is judged to be in a stable state.

[0041] Conversely, as shown in Fig. 5, if the real-time current waveform measured from the elevator does not match a small number of cluster waveforms, it means that the real-time current waveform 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 that the elevator is judged to be in a somewhat unstable state and an alarm is issued so that management such as inspection or repair can be performed in advance before a breakdown occurs in the elevator.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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, A predictive maintenance method for a device using a cluster waveform, characterized in that it includes an extraction process (S22) 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) for measuring the energy waveform of a device for real-time operation by collecting information on changes in energy size over time measured from a device performing real-time operation, and A search process (S32) for searching among a plurality of cluster waveforms for a waveform having a shape similar to the energy waveform of the device for real-time operation measured in the above measurement process (S31), A predictive maintenance method for a device using a cluster waveform, characterized in that it includes a judgment process (S33) for detecting the device as being in a stable state if a cluster waveform similar to the real-time energy waveform of the device is matched through the above search process (S32), and for detecting the device as being in an unstable state if a cluster waveform similar to the real-time energy waveform of the device is not matched.

4. 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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