KR102354568B1 - Method and system for detecting fault of swing device - Google Patents

Abstract

The present invention relates to an apparatus and method for diagnosing a malfunction of a turning device, comprising: a sensor unit for sensing a vibration signal from vibration of a gearbox; and a preprocessing unit for removing an external excitation component from the sensed vibration signal; , a filtering unit that receives the output signal of the preprocessor, removes the gear frequency, extracts only a difference signal, and a diagnosis unit that compares the difference signal with pre-stored soundness data to determine a failure of the turning device.

Description

Fault diagnosis device and method of swing device {Method and system for detecting fault of swing device}

The present invention relates to a turning device, and more particularly, to an apparatus and method for diagnosing a malfunction of the turning device .

Construction machinery, such as excavators, and military equipment, such as armored vehicles, include a lower body having a driving device, an upper body in which a working device is installed, and a turning device that connects the upper body and the lower body in a relatively rotatable manner.

According to the turning device, it is possible to work in various postures, which can contribute to the improvement of work efficiency, and it is common to include a turning motor, a turning bearing, and a turning device. The swing motor may be a hydraulic/electric swing motor as a driving source for providing power required for swing driving. The slewing bearing enables the relative rotation of the upper body through interaction with the slewing motor when the slewing motor is driven. As a result, it may be disposed between the slewing motor and the slewing bearing.

In the turning device as described above, a task requiring a relatively large force is repeated, and there are many cases where a large shock is transmitted from the outside, and thus fatigue damage and the like may occur. Such damage often occurs in gear teeth provided on the gear of the turning device or the turning bearing. Once the damage progresses, it becomes impossible to turn and work becomes impossible. When such an inability to work occurs, the owner incurs financial loss. In addition, if the turning device is damaged during operation, the turning drive/stop control becomes impossible and there is a risk that a safety accident may occur. Accordingly, it is necessary to preemptively diagnose the malfunction of the turning device to enable preventive maintenance, etc. However, since the internal structure and installation structure of parts such as the turning device are complicated, it is difficult to determine the failure unless a significant amount of damage has progressed. have. Therefore, it is necessary to develop a technology capable of diagnosing the failure/damage of the turning device more simply and efficiently.

It is an object of an embodiment of the present specification to provide an apparatus and method for diagnosing a malfunction of a turning device that can easily and efficiently diagnose a failure of the turning device.

A device for diagnosing a malfunction of a turning device according to an embodiment of the present specification includes a sensor unit that senses a vibration signal from vibration of a gearbox, a preprocessor that removes an external excitation component from the sensed vibration signal, and an output signal of the preprocessor It is characterized in that it comprises a filtering unit that receives the input, removes the gear frequency, extracts only the difference signal, and a diagnosis unit that compares the difference signal with pre-stored soundness data to determine the failure of the turning device.

The external excitation component is characterized in that the pump rotation frequency and the rotation motor rotation frequency.

The pre-processing unit includes a notch filter and a high-pass filter, and the filtering unit includes a notch filter or an Auto-Regressive (AR)-Minimum Entropy Deconvolution (MED) filter.

A fault diagnosis method of a fault diagnosis device for a turning device according to an embodiment of the present specification includes the steps of: a sensor sensing a vibration signal from vibration of a gearbox; removing an external excitation component from the vibration signal sensed by a preprocessor; , a filtering unit receiving the output signal of the preprocessing unit, removing the gear frequency, filtering only the difference signal, and a diagnosis unit comparing the difference signal with pre-stored soundness data to determine the failure of the turning device. do it with

The malfunction diagnosis apparatus according to an embodiment of the present invention senses the turning device vibration data, and filters the external excitation frequency and the gear frequency from the sensed vibration signal to obtain a difference signal. By comparing the acquired signal with pre-stored health data to determine whether there is a failure, the failure diagnosis can be effectively performed.

According to an embodiment of the present specification, the vibration of the gearbox is sensed to effectively diagnose the failure of the turning device only by filtering, so that the This is to prevent safety accidents.

1 is a block diagram of an apparatus for diagnosing a malfunction of a turning device according to an embodiment of the present specification.
2 is a circuit diagram illustrating an AR-MED filter employed in an embodiment of the present specification.
3 is a flowchart for explaining a fault diagnosis method of a fault diagnosis device of a turning device according to an embodiment of the present invention.
4 illustrates an example of vibration data according to an embodiment of the present invention.
5 is a graph illustrating signal filtering by a notch filter and a high-pass filter according to an embodiment of the present invention.
6 is a graph illustrating a signal that has undergone a pre-processing process according to an embodiment of the present invention.
7 is a graph illustrating a signal obtained by removing a gear frequency using a notch filter according to an embodiment of the present invention.
8 is a graph illustrating a signal from which a gear frequency is removed using an AR-MED filter according to an embodiment of the present invention.
9 is a table for explaining sanity data according to an embodiment of the present invention.

Hereinafter, embodiments of the present specification will be described in detail with reference to the accompanying drawings.

In describing the embodiments, descriptions of technical contents that are well known in the technical field to which the present specification belongs and are not directly related to the present specification will be omitted. This is to more clearly convey the gist of the present specification without obscuring the gist of the present specification by omitting unnecessary description.

For the same reason, some components are exaggerated, omitted, or schematically illustrated in the accompanying drawings. In addition, the size of each component does not fully reflect the actual size. In each figure, the same or corresponding elements are assigned the same reference numerals.

Hereinafter, an apparatus and method for diagnosing a malfunction of a turning device will be described with reference to embodiments and drawings of the present specification. As an example, the configuration and method of a malfunction diagnosis apparatus for a turning device will be described.

1 is a block diagram of a malfunction diagnosis apparatus for a turning device according to an embodiment of the present invention, and FIG. 2 is a circuit diagram illustrating an AR-MED filter employed in the failure diagnosis apparatus of FIG. 1 .

According to an exemplary embodiment of the present invention The malfunction diagnosis apparatus 100 of the turning device may include a sensor unit 110 , a pre-processing unit 120 , a filtering unit 130 , a diagnosis unit 140 , and a database unit 150 .

The sensor unit 110 senses a vibration signal from vibration of the gearbox when the excavator is driven. As will be described later, the failure diagnosis apparatus 100 of the turning device according to the present embodiment can determine whether a failure occurs only by processing a vibration signal. That is, since it is possible to determine whether there is a failure without installing an expensive component such as an encoder, it is possible to configure the malfunction diagnosis apparatus 100 of the turning device more inexpensively and simply.

The pre-processing unit 120 performs filtering to remove the external excitation component (A). The preprocessor 120 may be a notch filter and a high pass filter. The external excitation component may be a rotation frequency generated when the swing motor is driven and, in the case of a hydraulic swing device, a rotation frequency generated when a hydraulic pump for supplying hydraulic pressure is driven.

The filtering unit 130 removes the gear frequency corresponding to the regular signal from the signal from which the external excitation component is removed, and extracts only the difference signal that is the reference signal for failure determination.

The filtering unit 130 may employ a notch filter or an Auto-Regressive (AR)-Minimum Entropy Deconvolution (MED) filter.

For reference, the AR-MED filter is used to remove signals other than the impulse signal from the input signal. More specifically, as shown in FIG. 2 , the periodic signal is removed through the AR filter from the signal composed of the sum of the _{noise (N k} ), the periodic part (D _k ), and the impulse (G _{k ), and the filter coefficient is optimized} It is possible to extract the impulse signal in the input signal by finding the maximum kurtosis through .

When a general failure occurs, a peak signal may be generated in the vibration signal or a data crushing phenomenon called smearing may occur. However, since the energy of periodic signals such as gear frequency is very large, it becomes difficult to see these peak signals without additional processing. Accordingly, there is an advantage in that the peak signal can be found using the AR-MED filter.

The diagnosis unit 140 compares the failure determination reference signal obtained by the filtering unit 130 with the health data stored in the database unit 150 to determine the failure of the turning device. The health data will be described later.

When the diagnosis unit 140 determines that there is a failure, information about the failure may be provided by a preset device or means.

The preset device or means may include any one or more of a control server, a worker terminal, and an owner terminal.

A malfunction diagnosis apparatus according to an embodiment of the present invention senses vibration data of a turning device, and filters an external excitation frequency and a gear frequency from the sensed vibration signal. Accordingly, it is possible to easily acquire an impulse signal associated with a failure of the turning device from the sensed vibration signal. By comparing the acquired signal with pre-stored soundness data and determining whether there is a failure, it is possible to effectively diagnose the malfunction of the turning device.

3 is a flowchart for explaining a fault diagnosis method of a fault diagnosis device of a turning device according to an embodiment of the present invention, and FIGS. 4 to 8 are signals for fault diagnosis of a turning device according to an embodiment of the present invention. It is a diagram explaining filtering.

First, vibration data is acquired through the sensor unit (S110).

4 illustrates an example of the vibration data obtained in step S110. Fig. 4 (a) shows frequency components in the entire band, and Fig. 4 (b) shows an excerpt and enlarged view of an analysis target frequency component in the entire band.

As can be seen with reference to FIG. 4 , the vibration data acquired by the sensor unit includes a component of interest and an external excitation component.

Here, the component of interest means a rotation frequency generated by the rotation of gears installed in the turning device, and in FIG. 4 , multiple components A1 and A3 of the rotation frequency of the first gear in the turning device are shown. In this embodiment, a case in which the rotation frequency of the gear is 322 Hz will be described as an example. On the other hand, the external excitation component means the rotation frequency transmitted from external devices (pump, slewing motor, etc.) as described above. The multiple components of (C1, C2, C3) are shown as an example. In this embodiment, the rotation frequency of the pump is 215.6 Hz, the rotation frequency of the turning motor is 192.6 Hz, and a case in which three multiples of these components are shown will be described as an example.

An external excitation component is removed from the obtained vibration signal (S120).

In the present embodiment, a range of up to 5 multiples of a frequency (external excitation component) to be removed may be removed by using a notch filter or a high-pass filter. In the case of using a notch filter, as shown in (a) of FIG. 5, a range (about ±10% of the center frequency) including about 90% or more and about 110% or less of the rotation frequency of the turning motor is selected as a filtering target can do. Here, about ±10% is an exemplary range, and when the side band ranges of the swing motor and the pump frequency component become wider, it is preferable to adjust them wider. When a high-pass filter is used, as shown in FIG. 5(b), external excitation components can be removed by passing only a signal having a frequency of about 90% or more of the component of interest.

The preprocessed signal can be confirmed through FIG. 6 .

Fig. 6 (a) is an example showing an initially input frequency component, and Fig. 6 (b) is a graph showing a signal from which an external excitation component is removed by pre-processing. That is, FIG. 6A shows an input signal of the preprocessor, and FIG. 6B shows an output signal of the preprocessor. The output signal of the above-described preprocessor becomes the signal of FIGS. 7 and 8 (a), that is, the input signal, which will be described later.

The gear frequency (component of interest, GMF) corresponding to the regular signal is removed from the signal from which the external excitation component is removed, and only the difference signal corresponding to the failure judgment reference signal is extracted (S130).

The filter used here is either a notch filter or an AR-MED filter.

7 (a) is a graph showing the illustrated pre-processed vibration signal, and FIG. 7 (b) is a failure determination reference signal obtained by removing the gear frequency using a notch filter from the pre-processed vibration signal of (a). It is a graph representing a signal. That is, FIG. 7(a) shows an input signal of the notch filtering unit, and FIG. 7(b) shows an output signal of the notch filtering unit.

Here, the range of the notch filter may correspond to a component five times the rotation frequency of the gears installed in the turning device. In this embodiment, the case of 322 Hz as the rotation frequency of the first gear and 74 Hz as the second gear frequency will be described as an example. As described above, if the notch filter is used, it can be removed by setting the range up to the frequency that is ±10% of each center frequency.

Fig. 8 (a) is a graph showing the shown pre-processed vibration signal, and Fig. 8 (b) is a failure determination reference signal obtained by removing the gear frequency using an AR-MED filter from the pre-processed vibration signal of (a). It is a graph showing the difference signal. Fig. 8(a) shows an input signal of the AR-MED filtering unit, and Fig. 7(b) shows an output signal of the AR-MED filtering unit.

The diagnostic unit compares the difference signal with the health data previously stored in the database to determine whether there is a failure (S140).

It goes without saying that the health data may be updated periodically.

The health data is defined from the vibration signal of the gearbox in normal condition.

As can be seen with reference to FIG. 9 , sanity data adopted in an embodiment of the present invention are FM4, M6A, and M8A.

FM4 is soundness data on kurtosis.

FM4 can detect when one or two gears are damaged, but the detection sensitivity is lowered when all gears are damaged. Therefore, FM4 is useful for detecting early damage and may not be suitable for measuring the progression of damage. In order to secure these shortcomings, M6A and M8A are compared together. Since such health data is a known technique, a detailed description thereof will be omitted.

If the diagnostic unit determines that there is a failure, it notifies the failure using the connected output device or communication unit. For example, a fault is displayed on the instrument panel or an alarm sound is output. Alternatively, a failure is notified to a preset user terminal. At the time of notification, only a signal for notifying that there is a failure may be transmitted, and comparison data of whether failure in step S140 may be transmitted.

The above-described method may be implemented through various means. For example, embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.

In the case of implementation by hardware, the method according to embodiments of the present invention may include one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs). , field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and the like.

In the case of implementation by firmware or software, the method according to the embodiments of the present invention may be implemented in the form of a module, procedure, or function that performs the functions or operations described above. The software code may be stored in the memory unit and driven by the processor. The memory unit may be located inside or outside the processor, and may transmit/receive data to and from the processor by various well-known means.

The embodiments disclosed herein have been described above with reference to the accompanying drawings. As such, the embodiments shown in each drawing should not be construed as being limited, and may be combined with each other by those skilled in the art after reading the content of the present specification, and when combined, it may be interpreted that some components may be omitted.

Here, the terms or words used in the present specification and claims should not be construed as being limited to conventional or dictionary meanings, but should be interpreted as meanings and concepts consistent with the technical idea disclosed in the present specification.

Therefore, the embodiments described in the present specification and the configurations shown in the drawings are only one embodiment disclosed in the present specification, and do not represent all of the technical ideas disclosed in the present specification. It should be understood that there may be equivalents and variations.

100: fault diagnosis device
110: sensor unit
120: preprocessor
130: filtering unit
140: diagnostic unit
150: database unit

Claims (8)

a sensor unit for sensing a vibration signal from vibration of the gearbox;
a preprocessor for removing an external excitation component from the sensed vibration signal;
a filtering unit that receives the output signal of the preprocessor, removes a gear frequency corresponding to a regular signal, and extracts only a difference signal corresponding to a failure determination reference signal; and
A diagnosis unit that compares the difference signal with pre-stored soundness data to determine a failure of the turning device
includes,
AR (Auto-Regressive)-MED (Minimum Entropy Deconvolution) A device for diagnosing a malfunction of a turning device including a filter.
The apparatus for diagnosing a malfunction of a turning device according to claim 1, wherein the external excitation component is a pump rotation frequency and a turning motor rotation frequency.
The apparatus of claim 1, wherein the pre-processing unit includes a notch filter and a high-pass filter.
delete sensing, by a sensor, a vibration signal from vibration of the gearbox;
removing an external excitation component from the sensed vibration signal by the preprocessor;
filtering, by a filtering unit, receiving the output signal of the preprocessor, removing the gear frequency corresponding to the regular signal, and extracting only the difference signal corresponding to the failure determination reference signal; and
determining, by the diagnosis unit, the failure of the turning device by comparing the difference signal with previously stored health data
includes,
A malfunction diagnosis method of a turning device including an AR (Auto-Regressive)-MED (Minimum Entropy Deconvolution) filter.
6. The method of claim 5,
The fault diagnosis method of a turning device, characterized in that the external excitation component is a pump rotation frequency and a rotation motor rotation frequency.
[6] The method of claim 5, wherein the pre-processing unit includes a notch filter and a high-pass filter.
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