JP2023152346A - Plain bearing diagnostic assistance device, method, and program - Google Patents

Abstract

To provide a plain bearing diagnosis assistance device, plain bearing diagnosis assistance method, and plain bearing diagnosis assistance program, which allow for providing information usable when diagnosing the condition of a plain bearing.SOLUTION: A plain bearing diagnosis assistance device D of the present invention is configured to acquire time-series vibration data obtained by detecting vibration arising at a plain bearing, derive a predetermined feature quantity indicative of features of the acquired time-series vibration data, determine whether the derived feature quantity is in an increasing trend or decreasing trend, and output a result of the determination.SELECTED DRAWING: Figure 1

Description

The present invention relates to a slide bearing diagnosis support device, a slide bearing diagnosis support method, and a slide bearing diagnosis support program that support diagnosis of a slide bearing.

BACKGROUND ART In various types of devices that involve rotational motion, reciprocating motion, etc., bearings are used to support the shaft in order to smoothly move the shaft in response to the rotary motion, reciprocating motion, etc. If an abnormality occurs in this bearing, smooth movement will be inhibited, and there is a risk that the device will malfunction. Therefore, it is desired to diagnose the condition of the bearing. The bearings are generally classified into rolling bearings and sliding bearings. The rolling bearing is a device that supports a load by placing rolling elements such as balls and rollers between two members (shaft and bearing ring). The sliding bearing is a device in which the shaft and the surface of the bearing are in direct contact and the movement of the shaft is supported by the surface. A technique for diagnosing the condition of the rolling bearing is disclosed in Patent Document 1, for example.

Patent No. 6824076

By the way, research and understanding of the damage mechanism of the rolling bearing condition has been advanced, and there are various techniques for diagnosing the condition of the rolling bearing, as shown in Patent Document 1. However, the damage mechanism in the sliding bearing has not been elucidated, and it is desired to diagnose the condition of the sliding bearing.

The present invention has been made in view of the above-mentioned circumstances, and its purpose is to provide a bearing diagnosis support device, a sliding bearing diagnosis support method, and a sliding bearing diagnosis support device capable of providing information that can be used when diagnosing the condition of a sliding bearing. The objective is to provide a diagnostic support program.

As a result of various studies, the present inventors have found that the above object can be achieved by the following present invention. That is, the sliding bearing diagnosis support device according to one aspect of the present invention includes a data acquisition unit that acquires time-series vibration data obtained by detecting vibrations occurring in the sliding bearing, and a data acquisition unit that acquires time-series vibration data obtained by the data acquisition unit. On the other hand, a feature amount processing section that calculates a predetermined feature amount representing the characteristics of the vibration data, a trend determination section that determines whether the feature amount determined by the feature amount processing section has an increasing tendency or a decreasing tendency, and the trend determining section and an output unit that outputs the determination result determined by. Preferably, in the sliding bearing diagnosis support device described above, the data acquisition unit is an acceleration sensor or an AE (Acoustic Emission) sensor attached to the sliding bearing or a member that propagates vibrations generated in the sliding bearing.

Generally, a lubricated sliding bearing forms an oil film between the shaft and the sliding bearing, but due to changes in various conditions such as the load on the shaft and deterioration of the lubricating oil, so-called oil film depletion may occur. There is. This lack of oil film causes contact between the shaft and the sliding bearing, causing friction and wear, which is a cause of so-called seizure. According to the inventor's findings, in sliding bearings, after the phenomenon of friction and wear, wear particles are discharged and surface irregularities are scraped, thereby forming an oil film again and returning to a lubricated state. be. In the state of friction and wear, the vibrations generated in the sliding bearing tend to increase, and in the state where an oil film is formed again and the lubrication state returns, the vibrations tend to decrease. The bearing diagnosis support device outputs a determination result that determines whether the feature value has an increasing tendency or a decreasing tendency. By referring to the determination result, the user can determine whether the sliding bearing is in the above-mentioned friction and wear state. It can be determined whether an oil film is formed again and the state returns to a lubricated state. Therefore, by outputting the determination result, the sliding bearing diagnosis support device can provide information that can be used when diagnosing the condition of the sliding bearing.

In another aspect, in the sliding bearing diagnosis support device described above, the feature amount processing section obtains the feature amount after filtering the vibration data with a predetermined high-pass filter.

Since such a sliding bearing diagnosis support device filters vibration data using a predetermined high-pass filter, it is possible to remove a low frequency band occupied by noise components, thereby outputting a more reliable determination result.

In another aspect, in these above-described sliding bearing diagnosis support devices, the feature amount processing section obtains an effective value of the vibration data as the feature amount.

Since such a sliding bearing diagnosis support device obtains the effective value of the vibration data as the feature amount, the feature amount can be expressed as an average of one cycle of the vibration data.

In another aspect, in these above-mentioned sliding bearing diagnosis support devices, the feature amount processing unit is configured to perform a predetermined period of time before a predetermined diagnosis time in the time-series vibration data acquired by the data acquisition unit. The past vibration data is set as diagnosis target data, the feature quantity is calculated for the diagnosis target data, and the tendency determining unit calculates the average value of the feature quantity calculated for the diagnosis target data by the feature quantity processing unit and Based on the standard deviation, a threshold value for determining whether the above trend is increasing or decreasing is determined, and by using the determined threshold value, it is determined whether the feature amount determined by the feature amount processing unit is increasing trend or decreasing trend. .

Such a sliding bearing diagnosis support device determines a threshold value for determining whether there is an increasing trend or a decreasing trend based on the average value and standard deviation of the feature values determined for the diagnosis target data. The threshold value can be dynamically determined.

In another aspect, in the sliding bearing diagnosis support device described above, the threshold value is a first threshold value for determining whether or not there is the decreasing tendency, and a first threshold value for determining whether or not there is the increasing tendency. The second threshold value is two.

Since such a sliding bearing diagnosis support device calculates the first and second threshold values, it is possible to individually determine whether or not there is a decreasing trend and whether or not there is an increasing trend.

A sliding bearing diagnosis support method according to another aspect of the present invention includes a data acquisition step that is executed by a computer and acquires time-series vibration data of detected vibrations occurring in the sliding bearing; a feature amount processing step for obtaining a predetermined feature amount representing the characteristics of the vibration data for the series of vibration data; and a trend determination step for determining whether the feature amount obtained in the feature amount processing step has an increasing tendency or a decreasing tendency. , and an output step of outputting the determination result determined in the trend determination step.

A sliding bearing diagnosis support program according to another aspect of the present invention is executed by a computer, and includes a data acquisition step of acquiring time-series vibration data of detected vibrations occurring in the sliding bearing, and a data acquisition step of acquiring time-series vibration data obtained by detecting vibrations occurring in the sliding bearing; a feature amount processing step for obtaining a predetermined feature amount representing the characteristics of the vibration data for the series of vibration data; and a trend determination step for determining whether the feature amount obtained in the feature amount processing step has an increasing tendency or a decreasing tendency. , and an output step of outputting the determination result determined in the trend determination step.

Such a sliding bearing diagnosis support method and a sliding bearing diagnosis support program can provide information that can be used when diagnosing the condition of a sliding bearing by outputting the determination result.

The slide bearing diagnosis support device, the slide bearing diagnosis support method, and the slide bearing diagnosis support program according to the present invention can provide information that can be used when diagnosing the condition of a slide bearing.

FIG. 1 is a block diagram showing the configuration of a sliding bearing diagnosis support device in an embodiment. FIG. 2 is a schematic diagram for explaining the arrangement of an AE sensor (an example of a data acquisition unit 1) in the sliding bearing diagnosis support device. 3 is a flowchart showing the operation of the sliding bearing diagnosis support device. These are experimental results showing a specific example of the determination results of the sliding bearing diagnosis support device. 5 is a schematic diagram for explaining how to obtain the average value and standard deviation of feature amounts when obtaining the determination result shown in FIG. 4. FIG.

Hereinafter, one or more embodiments of the present invention will be described with reference to the drawings. However, the scope of the invention is not limited to the disclosed embodiments. It should be noted that structures with the same reference numerals in each figure indicate the same structure, and the description thereof will be omitted as appropriate. In this specification, when referring to a general term, a reference numeral without a subscript is used, and when referring to an individual configuration, a reference numeral with a suffix is used.

The slide bearing diagnosis support device in the embodiment is a device that supports diagnosis of the condition of the slide bearing by providing information (diagnosis support information) that can be used when diagnosing the condition of the slide bearing. This sliding bearing diagnosis support device includes a data acquisition section that acquires time-series vibration data obtained by detecting vibrations occurring in a sliding bearing, and a data acquisition section that acquires time-series vibration data obtained by detecting vibrations occurring in a sliding bearing, and a characteristic analysis of the vibration data for the time-series vibration data acquired by the data acquisition section. a feature amount processing unit that calculates a predetermined feature amount to represent, a trend determination unit that determines whether the feature amount determined by the feature amount processing unit is increasing or decreasing, and outputting a determination result determined by the trend determination unit. and an output section. Hereinafter, such a sliding bearing diagnostic support device, as well as a sliding bearing diagnostic support method and a sliding bearing diagnostic support program implemented therein will be described in more detail.

FIG. 1 is a block diagram showing the configuration of a sliding bearing diagnosis support device in an embodiment. FIG. 2 is a schematic diagram for explaining the arrangement of the AE sensor (an example of the data acquisition section 1) in the sliding bearing diagnosis support device.

For example, as shown in FIG. 1, the sliding bearing diagnosis support device D in the embodiment includes a data acquisition section 1, a preprocessing section 2, a control processing section 3, an input section 4, an output section 5, and an interface section. (IF unit) 6 and a storage unit 7.

The data acquisition unit 1 is a device that acquires time-series vibration data obtained by detecting vibrations occurring in a sliding bearing. The data acquisition unit 1 is a sensor such as an acceleration sensor or an AE (Acoustic Emission) sensor, in order to support the diagnosis of the diagnosis target in almost real time, and an appropriate sensor is used depending on the vibration frequency of the diagnosis target. be done. The acceleration sensor or AE sensor is attached to a sliding bearing or a member that propagates vibrations generated in the sliding bearing, and acquires the time-series vibration data by detecting the vibration at predetermined sampling intervals. For example, as shown in FIG. 2, the AE sensor 1, which is an example of the data acquisition unit 1, is attached to a mechanical equipment Ob (for example, its mechanism, housing, etc.) having a rotating shaft AX supported by a sliding bearing BR to be diagnosed. will be placed. As a result, vibrations of the sliding bearing BR generated in the sliding bearing BR and propagated to the mechanical equipment Ob are detected by the AE sensor 1. In this embodiment, the AE sensor 1 is connected to the control processing section 3 via the preprocessing section 2, and outputs its output to the preprocessing section 2.

Note that the data acquisition unit 1 is not limited to the above-mentioned acceleration sensor or AE sensor, but may be another device. For example, the data acquisition unit 1 is an interface circuit that inputs and outputs data to and from external equipment. The external device is a storage medium that stores the vibration data, such as a USB (Universal Serial Bus) memory and an SD card (registered trademark). Alternatively, the external device may be a CD-ROM (Compact Disc Read Only Memory), a CD-R (Compact Disc Recordable), or a DVD-ROM (Digital Versatile Disc Read On) that records the vibration data. ly Memory) and DVD- This is a drive device that reads data from a recording medium such as an R (Digital Versatile Disc Recordable). This interface circuit as the data acquisition section 1 may be connected to the external device by wire or wirelessly. Alternatively, the data acquisition unit 1 is, for example, a communication interface circuit that transmits and receives communication signals to and from an external device, and the external device is connected to a network (WAN (Wide Area Network, including public communication network)) or LAN ( The server device is connected to the communication interface circuit via a local area network (Local Area Network) and manages the vibration data. Such a data acquisition section 1 may be connected to the control processing section 3 without going through the preprocessing section 2. In such a data acquisition unit 1, a diagnosis target can be verified after generating the vibration data. Here, when the data acquisition section 1 is an interface circuit or a communication interface circuit, the data acquisition section 1 may also be used as the IF section 6 (that is, even if the IF section 6 is used as the data acquisition section 1). good).

The preprocessing unit 2 is a device that is connected to the control processing unit 3 and performs predetermined signal processing on the output of the data acquisition unit 1, and includes, for example, an amplifier that amplifies the output of the data acquisition unit 1 by a predetermined amplification factor. It is an AD converter or the like that converts the output of the data acquisition section 1 amplified by the amplifier from an analog signal to a digital signal. A so-called amplifier, a discriminator, or the like is used as the amplifier. The preprocessing section 2 outputs the output of the data acquisition section 1, which has undergone the predetermined signal processing, to the control processing section 3.

In the example shown in FIG. 1, the preprocessing unit 2 is configured with hardware, but may be configured with software. In this case, the preprocessing unit 2 is functionally configured in the control processing unit 3. good.

The input unit 4 is connected to the control processing unit 3, and inputs various commands such as a command to start diagnosis support, and information for operating the sliding bearing diagnosis support device D, such as the name of machinery and equipment to be diagnosed. It is a device for inputting various necessary data to the sliding bearing diagnosis support device D, and includes, for example, a plurality of input switches, a keyboard, a mouse, etc. to which predetermined functions are assigned. The output unit 5 is a device that is connected to the control processing unit 3 and outputs commands and data input from the input unit 4, judgment results determined as described below, etc. under the control of the control processing unit 3. , for example, display devices such as CRT displays, liquid crystal displays, and organic EL displays, and printing devices such as printers.

Note that the input section 4 and the output section 5 may constitute a so-called touch panel. When configuring this touch panel, the input section 4 is a position input device that detects and inputs an operating position, such as a resistive film type or a capacitive type, and the output section 5 is a display device. In this touch panel, the position input device is provided on the display surface of the display device, one or more input content candidates that can be input are displayed on the display device, and a display displaying the input content that the user wants to input is displayed. When a position is touched, the position is detected by the position input device, and the display content displayed at the detected position is input to the sliding bearing diagnosis support device D as the user's operation input content. With such a touch panel, it is easy for a user to intuitively understand input operations, so that a sliding bearing diagnosis support device D that is easy for the user to handle is provided.

The IF section 6 is a circuit that is connected to the control processing section 3 and performs input/output of data with an external device under the control of the control processing section 3, and is, for example, an interface circuit for RS-232C, which is a serial communication method. , an interface circuit using the Bluetooth (registered trademark) standard, an interface circuit that performs infrared communication such as the IrDA (Infrared Data Association) standard, and an interface circuit using the USB (Universal Serial Bus) standard. Further, the IF section 6 is a circuit that performs communication with an external device, and may be, for example, a data communication card, a communication interface circuit according to the IEEE802.11 standard, or the like.

The storage section 7 is a circuit that is connected to the control processing section 3 and stores various predetermined programs and various predetermined data under the control of the control processing section 3. The various predetermined programs include, for example, a control processing program, and the control processing program controls each section 1, 2, 4 to 7 of the sliding bearing diagnosis support device D according to the function of each section. A control program that calculates a predetermined feature amount representing the characteristics of the vibration data from the time-series vibration data acquired by the data acquisition unit 1, and a feature amount processing program that calculates a predetermined feature amount representing the characteristics of the vibration data, and a feature amount processing program that calculates the feature amount obtained by the feature amount processing program. This includes a trend determination program that determines whether there is an increasing trend or a decreasing trend. The various kinds of predetermined data include, for example, data necessary for executing each of these programs, such as vibration data acquired by the data acquisition section 1. Such a storage unit 7 includes, for example, a ROM (Read Only Memory) that is a nonvolatile storage element, an EEPROM (Electrically Erasable Programmable Read Only Memory) that is a rewritable nonvolatile storage element, and the like. The storage unit 7 includes a RAM (Random Access Memory), which serves as a so-called working memory of the control processing unit 3 that stores data generated during execution of the predetermined program. Note that the storage unit 7 may include a hard disk device capable of storing a large amount of data.

The control processing section 3 is a circuit that controls each section 1, 2, 4 to 7 of the sliding bearing diagnosis support device D according to the function of each section, and supports diagnosis of the state of the sliding bearing. The control processing unit 3 includes, for example, a CPU (Central Processing Unit) and its peripheral circuits. The control processing section 3 is functionally configured with a control section 31, a feature amount processing section 32, and a tendency determination section 33 by executing the control processing program.

The control section 31 controls each section 1, 2, 4 to 7 of the sliding bearing diagnosis support device D according to the function of each section, and controls the entire sliding bearing diagnosis support device D.

The feature amount processing section 32 calculates a predetermined feature amount representing the characteristics of the vibration data from the time-series vibration data acquired by the data acquisition section 1. In this embodiment, the feature amount processing unit 32 filters the vibration data using a predetermined high-pass filter, and then obtains the effective value of the vibration data as the feature amount. More specifically, the feature amount processing unit 32 sets vibration data in the past for a predetermined length of time before a predetermined diagnosis time in the time-series vibration data acquired by the data acquisition unit 1 as data to be diagnosed; The feature quantity is obtained for the diagnosis target data. The predetermined diagnosis time point may be set arbitrarily; for example, when diagnosing in approximately real time, the predetermined diagnosis time point may be set as the current time point (current time, current sampling timing, current diagnosis support timing (=timing for obtaining the determination result)). is set to

In this embodiment, the feature amount processing section 32 functionally includes a high-pass filter section 321 and a feature amount generation section 322.

The high-pass filter section 321 filters the vibration data using a predetermined high-pass filter having a preset cutoff frequency. More specifically, in the present embodiment, the feature amount processing unit 32 stores the detection results detected by the AE sensor 1 at predetermined sampling intervals in the storage unit 7, and stores each detection result for a predetermined time length set in advance. The result is used as the diagnosis target data in the time-series vibration data, and the high-pass filter section 321 filters the diagnosis target data. The high-pass filter allows signal components in a frequency band equal to or higher than the cutoff frequency to pass through the input signal, and removes signal components in a frequency band lower than the cutoff frequency. Note that since the high-pass filter is used to remove a low frequency band occupied by noise components, a band-pass filter may be used instead of the high-pass filter.

The feature quantity generation unit 322 obtains the feature quantity of the vibration data filtered by the high-pass filter unit 321. In this embodiment, the feature amount generation unit 322 obtains the effective value of the vibration data as the feature amount. More specifically, the feature amount generation unit 322 obtains the effective value of the diagnosis target data as the feature amount. Note that the feature quantity is not limited to the effective value, but may be other quantities as long as it represents the characteristics of the vibration data, for example, from one peak in the vibration data to the adjacent peak. It may be a peak-to-peak value (p-p value), which is a time interval to another peak, or it may be an event count number, which is a count of predefined predetermined events. The predetermined event is, for example, that the vibration data exceeds a preset threshold, and the number of times the vibration data exceeds the threshold is used as the feature amount.

The trend determining section 33 determines whether the feature amount obtained by the feature amount processing section 32 has an increasing tendency or a decreasing tendency. In the present embodiment, the trend determining unit 33 determines whether the increasing trend or the decreasing trend is based on the average value and standard deviation of the feature amounts obtained for the diagnosis target data by the feature amount processing unit 32. A threshold value is determined, and by using the determined threshold value, it is determined whether the feature amount determined by the feature amount processing section 32 has an increasing tendency or a decreasing tendency. In the present embodiment, the threshold values include, for example, two threshold values: a first threshold value for determining whether or not there is a decreasing trend, and a second threshold value for determining whether or not there is an increasing trend. be.

In this embodiment, the trend determination section 33 functionally includes a threshold generation section 331 and a determination section 332.

The threshold value generation unit 331 calculates a threshold value for determining whether the data is increasing or decreasing based on the average value and standard deviation of the feature values obtained for the diagnosis target data by the feature value processing unit 32. It is. More specifically, in the present embodiment, the threshold generation unit 331 subtracts the standard deviation σ from the average value μ of the feature values to determine whether or not there is a decreasing tendency. By generating a threshold Th1 (Th1 = μ - σ) and adding the standard deviation σ to the average value μ of the feature amount, a second threshold Th2 for determining whether there is an increasing tendency is generated. (Th2=μ+σ).

The determining unit 332 uses the threshold generated by the threshold generating unit 331 to determine whether the feature amount obtained by the feature amount processing unit 32 has an increasing tendency or a decreasing tendency. More specifically, in the present embodiment, when the feature value SV obtained by the feature value processing unit 32 is lower than the first threshold Th1 generated by the threshold value generation unit 331 (SV<Th1 (=μ−σ)), it is determined that there is a decreasing tendency, and the feature value SV obtained by the feature value processing unit 32 is higher than the second threshold Th2 generated by the threshold value generation unit 331 (SV> Th2 (= μ + σ)) is determined to be the increasing trend, and the feature value SV obtained by the feature value processing unit 32 is equal to or greater than the first threshold Th1 generated by the threshold value generation unit 331 and is equal to or higher than the second threshold Th2. If (Th1≦SV≦Th2), the trend determined previously (for example, last time) is maintained.

The control unit 31 outputs the determination result determined by the trend determination unit 33 from the output unit 5.

These control processing section 3, input section 4, output section 5, IF section 6, and storage section 7 can be configured by, for example, a desktop, notebook, or tablet computer. Note that when the data acquisition section 1 is an interface circuit or a communication interface circuit, the IF section 6 can also be used as the data acquisition section 1, so that the sliding bearing diagnosis support device D including the data acquisition section 1 can be configurable by

Next, the operation of this embodiment will be explained. FIG. 3 is a flowchart showing the operation of the sliding bearing diagnosis support device.

When the sliding bearing diagnosis support device D having such a configuration is turned on, it initializes each necessary part and starts its operation. The control processing section 3 is functionally configured with a control section 31, a feature amount processing section 32, and a trend determination section 33 by executing the control processing program, and the feature amount processing section 32 is functionally configured with a high-pass filter section 321 and a feature amount processing section 32. The quantity generation section 322 is functionally configured, and the trend determination section 33 is functionally configured with a threshold generation section 331 and a determination section 332.

When the sliding bearing diagnostic support device D receives a command instructing the start of diagnostic support at the input unit 4, it performs each process S1 to S11 shown in FIG. 3 until the input unit 4 receives a command instructing the end of the diagnostic support. Repeat and execute at predetermined sampling intervals. Here, the diagnosis target data cannot be generated until the detection results of the AE sensor 1, which is an example of the data acquisition unit 1, are acquired for the predetermined time length. The process of acquiring the detection results of the AE sensor 1 and storing them in the storage unit 7 is repeatedly executed at the predetermined sampling interval until the time period elapses.

When the predetermined length of time has elapsed from the start of the diagnosis support and the sampling timing has arrived, processing at the current sampling timing is started. In FIG. The control unit 31 acquires the detection result from the data acquisition unit 1 (AE sensor 1 in this example) via the preprocessing unit 2, and stores it in the storage unit 7 (S1). The preprocessing unit 2 performs predetermined signal processing such as amplification and AD conversion on the output (detection result) of the data acquisition unit 1, and controls the output of the data acquisition unit 1 that has undergone the predetermined signal processing. It is output to the processing section 3.

Subsequently, the sliding bearing diagnosis support device D generates diagnosis target data by the feature amount processing unit 32 of the control processing unit 3 (S2). More specifically, the feature amount processing unit 32 sets the current sampling timing as the predetermined diagnosis time point, and from the detection results obtained in process S1, past detection results for the predetermined length of time are used as the diagnosis target. Data.

Subsequently, the sliding bearing diagnosis support device D performs a filtering process on the diagnosis target data generated in process S2 using the high-pass filter section 321 in the feature quantity processing section 32 of the control processing section 3 (S3).

Subsequently, the sliding bearing diagnosis support device D uses the feature amount generating section 322 in the feature amount processing section 32 of the control processing section 3 to obtain the feature amount SV of the diagnosis target data filtered by the high-pass filter section 321, and stores the feature amount SV in the storage section 7. (S4). In this embodiment, the feature value generation unit 322 obtains the effective value of the diagnosis target data as the feature value SV.

Subsequently, the sliding bearing diagnosis support device D uses the threshold generation unit 331 in the tendency determination unit 33 of the control processing unit 3 to calculate the average value μ and the feature quantity calculated for the diagnosis target data by the feature quantity processing unit 32 Based on the standard deviation σ, a threshold value for determining whether the trend is increasing or decreasing is determined (S5). In this embodiment, the threshold generation unit 331 calculates a first threshold Th1 for determining whether or not there is a decreasing tendency, and a second threshold Th2 for determining whether or not there is an increasing tendency. . For example, from the current sampling timing, the average value μ and standard deviation σ of each feature obtained at each past sampling timing for the predetermined length of time are determined, and the first and Second threshold values Th1 and Th2 are determined. Note that in order to obtain the average value μ and standard deviation σ of the feature amounts, the length of time (period) to go back in the past is appropriately set in advance.

Subsequently, in the sliding bearing diagnosis support device D, the determination unit 332 in the tendency determination unit 33 of the control processing unit 3 determines that the feature value SV obtained by the feature value processing unit 32 in process S4 is generated by the threshold value generation unit 331 in process S5. It is determined whether or not the value is lower than the first threshold Th1 (S6). As a result of this determination, if the feature amount SV is lower than the first threshold Th1 (YES), the determination unit 332 determines that there is a decreasing trend as the current determination result, and stores it in the storage unit 7. (S7), and then process S11 is executed. On the other hand, as a result of the determination, if the feature amount SV is not lower than the first threshold Th1 (NO), that is, if the feature amount SV is greater than or equal to the first threshold Th1 (NO), the determination unit 332 then executes process S8.

In this process S8, the sliding bearing diagnosis support device D determines that the feature value SV obtained by the feature value processing unit 32 in process S4 exceeds the second threshold Th2 generated by the threshold value generation unit 331 in process S5. Determine whether or not there is. As a result of this determination, if the feature amount SV exceeds the second threshold Th1 (YES), the determination unit 332 determines that there is an increasing tendency as the current determination result, and stores it in the storage unit 7. (S9), and then process S11 is executed. On the other hand, as a result of the determination, if the feature amount SV is not higher than the second threshold Th2 (NO), that is, if the feature amount SV is less than or equal to the second threshold Th2 (NO), the determination unit 332 then executes process S10.

In this process S10, the sliding bearing diagnosis support device D causes the determination unit 332 to store the previous determination result as the current determination result in the storage unit 7, and then executes process S11. Note that this process S10 is executed when the feature amount SV is greater than or equal to the first threshold Th1 and less than or equal to the second threshold Th2.

In this process S11, the sliding bearing diagnosis support device D outputs the current determination result to the output unit 5 by the control unit 31, and ends this process at the current sampling timing. Note that the current determination result may be output from the IF section 6 to an external device, if necessary.

Regarding diagnosis support, the sliding bearing diagnosis support device D operates as described above.

As a specific example, the results obtained using the sliding bearing diagnosis support device D will be explained in the case where a bearing load testing device is actually used to load a sliding bearing and the sliding bearing is rotated.

FIG. 4 shows experimental results showing a specific example of the determination results of the sliding bearing diagnosis support device. The horizontal axis in FIG. 4 is the elapsed time [min] from the start of diagnostic support, and the vertical axis is the effective value of the diagnosis target data as an example of the feature amount SV. In this example, the AE sensor 1 is the effective value [V] of the AE signal. FIG. 5 is a schematic diagram for explaining how to obtain the average value and standard deviation of the feature amounts when obtaining the determination results shown in FIG. 4. The upper part of FIG. 5 shows the effective values x1, x2, x3, ... of the AE signal obtained every second, and the middle part of FIG. 5 shows the median values X1, X2, X3 of the effective value xk every 30 seconds. , ..., and the lower part of FIG. 5 shows the average values μ1, μ2, μ3, ... and standard deviations σ1, σ2, of each median value Xm in the past 5 minutes from the current time (current diagnosis support timing). σ3,... is shown.

In this specific example, an AE signal is sampled from the AE sensor 1 at a sampling frequency of 5 MHz, this sampled AE signal is amplified by 30 dB by a discriminator, and this amplified AE signal is amplified by a high-pass filter with a cutoff frequency of 200 kHz. The filtered AE signals were subjected to filtering processing, and the effective values x1, x2, x3, . . . of the filtered AE signals were obtained every second as shown in the upper part of FIG. In determining the tendency, in this specific example, as shown in the middle part of FIG. 5, the median value Xm of the effective values xk is obtained every 30 seconds as a representative value, and the effective values x1, x2, x3,... were thinned out. That is, for 30 seconds, the median value Xm of the 30 effective values xk determined every second was determined as the effective value representing the 30 seconds. Then, as shown in the lower part of FIG. 5, the average value μ and standard deviation σ of each median value Xm obtained in the past 5 minutes were obtained from the current diagnosis support timing. In this specific example, as can be seen from the above, each diagnosis support timing is every 30 seconds, the representative value obtained every 30 seconds is the feature amount SV, and the first threshold Th1 is Th1=μ− The second threshold Th2 was determined by Th2=μ+σ. Each determination result at each diagnosis support timing in this specific example is shown in FIG. In FIG. 4, time zones determined to have an increasing tendency are displayed with relatively dark hatching, and time zones determined to have a decreasing tendency are displayed with relatively light hatching. As can be seen from the above, the first and second thresholds Th1 and Th2 cannot be determined and the determination process cannot be executed for 5 minutes from the start of diagnostic support. Therefore, for convenience, FIG. It is said that

Generally, a lubricated sliding bearing forms an oil film between the shaft and the sliding bearing, but due to changes in various conditions such as the load on the shaft and deterioration of the lubricating oil, so-called oil film depletion may occur. There is. This lack of oil film causes contact between the shaft and the sliding bearing, causing friction and wear, which is a cause of so-called seizure. According to the inventor's findings, in sliding bearings, after the phenomenon of friction and wear, wear particles are discharged and surface irregularities are scraped, thereby forming an oil film again and returning to a lubricated state. be. In the state of friction and wear, the vibrations generated in the sliding bearing tend to increase due to the increase in friction and wear, while in the state where an oil film is formed again and the lubrication state is returned, the growth of the oil film causes the vibrations to increase. shows a decreasing trend. Note that the inventor named this phenomenon of forming an oil film again and returning to a lubricated state as a "running-in phenomenon."

As can be seen from Fig. 4, the feature value SV (the representative value in this example) repeatedly increases and decreases, which is thought to be due to the repeated formation and depletion of an oil film between the rotating shaft and the sliding bearing. If the feature value SV tends to increase, it can be interpreted as oil film breakage (friction, wear process), and if it tends to decrease, it can be interpreted as oil film formation. In FIG. 4, it can be seen that the first and second threshold values Th1 and Th2 are switched in accordance with the increase/decrease of the feature value SV, and it is considered that it is possible to diagnose the lubrication state of the sliding bearing.

As explained above, the sliding bearing diagnosis support device D according to the embodiment, the sliding bearing diagnosis support method and the sliding bearing diagnosis support program implemented therein are based on the determination result of determining whether a predetermined feature value has an increasing tendency or a decreasing tendency. By referring to the determination result, the user can determine whether the sliding bearing is in the state of friction and wear, or is in the state of forming an oil film again and returning to the lubricated state. Therefore, the slide bearing diagnosis support device D, the slide bearing diagnosis support method, and the slide bearing diagnosis support program can provide information that can be used when diagnosing the condition of the slide bearing by outputting the determination results.

The sliding bearing diagnosis support device D, the sliding bearing diagnosis support method, and the sliding bearing diagnosis support program filter the vibration data using a predetermined high-pass filter, so that the low frequency band occupied by noise components can be removed, resulting in more reliable Can output high judgment results.

Since the slide bearing diagnosis support device D, the slide bearing diagnosis support method, and the slide bearing diagnosis support program calculate the effective value of vibration data as the feature amount, the feature amount can be expressed as the average of one cycle of vibration data. .

The sliding bearing diagnosis support device D, the sliding bearing diagnosis support method, and the sliding bearing diagnosis support program determine whether there is an increasing trend or a decreasing trend based on the average value and standard deviation of the feature values obtained for the diagnosis target data. Therefore, the threshold value can be dynamically determined depending on the current situation.

Since the sliding bearing diagnosis support device D, the sliding bearing diagnosis support method, and the sliding bearing diagnosis support program calculate the first and second threshold values, it is possible to determine whether or not there is a decreasing trend, and whether or not there is an increasing trend. can be determined individually.

In order to express the present invention, the present invention has been adequately and fully described through embodiments with reference to the drawings in the above description, but those skilled in the art will easily be able to modify and/or improve the embodiments described above. It should be recognized that this is possible. Therefore, unless the modification or improvement made by a person skilled in the art does not depart from the scope of the claims stated in the claims, such modifications or improvements do not fall outside the scope of the claims. It is interpreted as encompassing.

D Sliding bearing diagnosis support device 1 Data acquisition unit 3 Control processing unit 5 Output unit 31 Control unit 32 Feature processing unit 33 Trend determination unit 321 High-pass filter unit 322 Feature generation unit 331 Threshold generation unit 332 Determination unit

Claims (7)

a data acquisition unit that acquires time-series vibration data obtained by detecting vibrations occurring in the sliding bearing;
a feature amount processing unit that calculates a predetermined feature amount representing the characteristics of the vibration data from the time-series vibration data acquired by the data acquisition unit;
a trend determining unit that determines whether the feature amount obtained by the feature amount processing unit is increasing or decreasing;
an output unit that outputs the determination result determined by the trend determination unit;
Sliding bearing diagnosis support device. The feature amount processing unit calculates the feature amount after filtering the vibration data with a predetermined high-pass filter.
The sliding bearing diagnosis support device according to claim 1. The feature amount processing unit obtains an effective value of the vibration data as the feature amount.
The sliding bearing diagnosis support device according to claim 1. The feature amount processing unit sets vibration data in the past for a predetermined time period before a predetermined diagnosis time in the time-series vibration data acquired by the data acquisition unit as diagnosis target data, and performs processing on the diagnosis target data. , find the feature amount,
The trend determining unit determines a threshold value for determining whether the increasing trend or decreasing trend is based on the average value and standard deviation of the feature quantities determined for the diagnosis target data by the feature quantity processing unit, and determining whether the feature amount obtained by the feature amount processing unit has an increasing tendency or a decreasing tendency by using the obtained threshold value;
A sliding bearing diagnosis support device according to any one of claims 1 to 3. The threshold values are two, a first threshold value for determining whether or not there is a decreasing trend, and a second threshold value for determining whether or not there is an increasing trend.
The sliding bearing diagnosis support device according to claim 4. executed by a computer,
a data acquisition step of acquiring time-series vibration data of detected vibrations occurring in the sliding bearing;
a feature amount processing step of calculating a predetermined feature amount representing the characteristics of the vibration data for the time-series vibration data acquired in the data acquisition step;
a trend determination step of determining whether the feature amount obtained in the feature amount processing step has an increasing tendency or a decreasing tendency;
an output step of outputting the determination result determined in the trend determination step;
Plain bearing diagnosis support method. executed by a computer,
a data acquisition step of acquiring time-series vibration data of detected vibrations occurring in the sliding bearing;
a feature amount processing step of calculating a predetermined feature amount representing the characteristics of the vibration data for the time-series vibration data acquired in the data acquisition step;
a trend determination step of determining whether the feature amount obtained in the feature amount processing step has an increasing tendency or a decreasing tendency;
an output step of outputting the determination result determined in the trend determination step;
Sliding bearing diagnosis support program.

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