JP5765881B2 - Equipment diagnostic equipment - Google Patents

Description

  The present invention relates to a diagnostic apparatus for equipment that collects data based on signals from sensors installed in the equipment and diagnoses equipment based on the collected data.

  Multiple equipment installed in factories, such as rotating equipment such as pumps and fans, are installed on these devices and monitored online, and the data obtained from the sensors is set to the reference value set by the user. A method of determining an abnormality of each device by comparison is common, and various methods have been proposed.

  As such a technique, for example, in Patent Document 1, the vibration waveform of a valve is collected and subjected to frequency analysis, and the frequency spectrum determined as a normal state is subtracted from the frequency spectrum of the vibration waveform obtained in the subsequent measurement. An abnormality diagnosis method for an apparatus is disclosed in which a cullback information amount is calculated as a frequency domain parameter from the remaining spectrum component data, and the presence / absence of abnormality is determined using this (see Patent Document 1 [0019]. ]reference).

JP 2001-318031 A

Liu Nobuyoshi and four others, “Abnormal Diagnosis of Rotating Machines Based on Symmetric Cullback Information”, Journal of the Japan Institute of Equipment Management, 1998, Vol. 10, No. 3, p. 22-27

Various equipment is installed in the factory, and the following two requirements are necessary to accurately diagnose these various equipment. First, the analysis program used for diagnosis is compatible with the characteristics of the equipment to be diagnosed, and second, the data used for diagnosis can be appropriately acquired.
However, the method disclosed in Patent Document 1 described above does not necessarily satisfy the first and second requirements. Hereinafter, this point will be described in detail.

<About the first requirement>
The method described in Patent Document 1 is a method of performing frequency analysis of a collected vibration waveform and obtaining a difference between frequency spectra of a normal state and an abnormal state as a Cullback information amount. However, for example, when the vibration waveform in the normal state is small, such as the vibration waveform of the intake valve of the plunger pump, even if abnormal vibration occurs at the time of diagnosis, the abnormal component is difficult to appear, and therefore There is a problem that it is difficult to detect.
Hereinafter, this problem will be described in detail by taking an intake / discharge valve of a plunger type pump as an example.

FIG. 10 shows an acceleration waveform when an acceleration sensor is installed on the suction / discharge valve of the plunger type pump. FIG. 10 (a) shows a normal acceleration waveform, and FIG. 10 (b) shows an abnormal state. Specifically, it is an acceleration waveform in a state where a leak occurs due to wear of the suction valve. 11 shows a graph of the probability density function based on the acceleration waveform shown in FIG. 10, FIG. 11 (a) shows a normal time corresponding to FIG. 10 (a), and FIG. An abnormal time corresponding to 10 (b) is shown. Further, FIG. 12 shows a state in which FIGS. 11 (a) and 11 (b) are superimposed.
As can be understood from FIG. 12, there is a difference in the probability density function between the normal time and the abnormal time.

By representing the probability density function as shown in FIG. 12, it is possible to visually compare the normal time and the abnormal time. However, in this state, it is not yet digitized, and automatic determination cannot be performed. Therefore, it is necessary to digitize. This is the amount of information (hereinafter sometimes referred to as “KL”), and this amount of cullback information is defined by the following equation (1).

However, as can be understood from the above equation (1), when the value of the probability density function of the vibration waveform in the normal state is small, the cullback information amount is the KL value even if the value of the probability density function at the time of diagnosis is large. As a result, it becomes small and it becomes difficult to detect an abnormality.
As described above, even if KL is used for diagnosis as shown in Patent Document 1, there is a problem that it is incompatible when the value of the probability density function of the vibration waveform in a normal state is small.

<About the second requirement>
The apparatus abnormality diagnosis method disclosed in Patent Document 1 is based on the premise that appropriate data can be acquired from a sensor installed in a facility device. It is a proposal about what can be done.
In order to perform an accurate diagnosis, it is an indispensable condition that data used for diagnosis can be acquired appropriately. However, Patent Document 1 does not describe a technique for acquiring data appropriately. If the data to be acquired is not appropriate, an accurate diagnosis cannot be made even if a diagnostic program based on the data is excellent.
Hereinafter, the importance of the acquired data will be described in detail.

Various devices are installed in the factory. For example, there are rotating devices that rotate at a low speed and those that rotate at a high speed. Also, there are high frequency and low frequency vibration frequencies.
For example, if there are three types of rotation speeds of 3600 rpm, 100 rpm, and 60 rpm, the time required for each rotation is 0.016 s, 0.60 s, and 1.0 s. It is. When the data to be acquired is vibration data, sampling times of 0.1 s, 1.0 s, and 2.0 s or more are required to accurately acquire the data, respectively. That is, the data sampling time may be short for high-speed rotation, but the data sampling time needs to be long for low-speed rotation.

  However, there is no known data collection device that can change the sampling time appropriately. For example, appropriate data collection is not possible for all rotating devices from low speed rotation to high speed rotation. Therefore, for example, an inefficient diagnosis may be performed in which a low-speed rotating device is excluded from the online monitoring and is separately diagnosed offline.

As for the sampling frequency, the sampling frequency may be a low frequency when the vibration frequency is low, but the sampling frequency needs to be a high frequency when the frequency is high. Therefore, in order to perform online monitoring for various equipment, it is required to set a sampling frequency suitable for each equipment.
However, in the conventional data collection device, since the sampling frequency is predetermined, it is difficult to perform optimum sampling for the device. Of course, for example, it is possible to acquire the sampling frequency at a high frequency for a low-frequency one, but since the sampling time is long for a low-frequency one, if sampling is performed at a high frequency for such one Therefore, the amount of data becomes enormous, the storage capacity becomes enormous, and the data processing takes a long time.

  In addition, in a single device, for example, in which a reduction gear is installed on the rotating shaft of an electric motor, accurate diagnosis of the entire device can be performed only by diagnosing the electric motor and the reduction gear at the same time. However, since the rotation speed differs between the electric motor and the speed reducer, the optimum sampling time and sampling frequency are different from each other. Therefore, only when optimum data sampling is performed at the same time, accurate diagnosis of the entire apparatus is possible. However, there is nothing that can achieve this.

  In the above description, the rotary machine has been described as an example. However, the above-described problem is not limited to the rotary machine, for example, a plunger pump that performs an intermittent operation, or a press machine that performs an intermittent operation or an instantaneous operation. The same applies to a reciprocating compressor or the like.

As described above, in order to acquire data of various devices and perform an accurate diagnosis, the analysis program used for the analysis must be suitable for the characteristics of the equipment to be diagnosed (first requirement), and It is an indispensable condition to be able to appropriately acquire data that is a premise of diagnosis (second requirement).
However, the method described in Patent Document 1 is not compatible with, for example, a suction / discharge valve of a plunger-type pump, and the data sampling time and sampling frequency cannot be set appropriately. I can't.

  The present invention has been made to solve the above-described problems, and is compatible with equipment that does not easily generate vibration waveforms during normal operation, and can accurately diagnose these equipment. An object of the present invention is to provide a diagnostic apparatus for equipment that can be used.

The inventor examined an analysis program that can accurately determine an abnormality of a facility device that is difficult to generate a vibration waveform in a normal state, such as the suction / discharge valve of the plunger pump described above, and was displayed in Non-Patent Document 1. The symmetric cullback information amount defined by the following formula (2) (hereinafter, sometimes referred to as “ID”) is focused.

  By using the above equation (2), the probability density function of the vibration waveform in the normal state: even if the value of Pr (t) is small, the probability density function of the vibration waveform at the time of diagnosis: the value of Pt (t) It was possible to grasp accurately, and in particular, we obtained knowledge that it is effective for diagnostic target devices that have a small value of the probability density function Pr (t) of the vibration waveform in a normal state, and confirmed this point.

FIG. 13 is a graphical representation of the distribution of KL values (FIG. 13 (a)) and ID values (FIG. 13 (b)) calculated based on the probability density function shown in FIG. The KL value and ID value correspond to the area surrounded by the curve shown in the graph of FIG.
As understood from the graph of FIG. 13, the KL value is almost zero on both sides of the acceleration (horizontal axis) 0 and does not appear as a numerical value (see FIG. 13A), but the ID value is a numerical value at the same position. Appears remarkably (see FIG. 13B). This means that the state of the actual equipment shown in FIG. 10 can be accurately captured.
As described above, even when the vibration waveform in the normal state is small, for example, the vibration waveform of the intake valve of the plunger pump, the abnormal vibration is accurately captured by using the symmetric cullback information amount. The knowledge that it can be obtained.

Even if a symmetric cullback information amount is used as an analysis program, it is necessary to be able to accurately acquire the data that is the premise thereof. Therefore, the inventor also examined the data collection device, and by using a data collection device that can be set to a sampling frequency and a sampling time suitable for the object to be diagnosed, an accurate equipment device combined with the use of the analysis program. The knowledge that the diagnosis of becomes possible.
The present invention is based on such knowledge, and specifically comprises the following configuration.

(1) A facility device diagnosis apparatus according to the present invention receives a signal from a sensor installed in a diagnosis target device, collects data, and transmits / receives data to / from the data collection device. A diagnostic device for equipment with a monitoring computer,
The data collection device includes a data acquisition circuit that inputs a signal output from a sensor and acquires data, and a sampling condition that inputs an instruction signal input from the outside and sets a data sampling condition for the data acquisition circuit Setting means and data transmission means for transmitting the sampled data to the outside,
According to the instruction signal, the sampling condition setting means is configured to pass a sampling time of data input from the sensor, a sampling frequency, a sensor that performs simultaneous sampling among a plurality of sensors, and which of a high-pass filter and a low-pass filter. Set filter switching ,
The monitoring computer comprises sampling condition input means for inputting sampling conditions by the data collection device, and diagnostic processing means for diagnosing the state of equipment based on data transmitted from the data collection device,
The sampling condition input means is configured so that the operator can input the sampling time, sampling frequency, simultaneous sampling target, filter switching by the input means for each sensor of the data collection device,
The diagnostic processing means, wherein the data acquisition device based on the sampled data normalized in the rms value of the diagnostic object both its waveform when collecting waveform in operating state of the apparatus, the waveform acquisition means for dimensionless, the From the probability density function calculating means for calculating the probability density function of the waveform sampled by the waveform sampling means, and the probability density function at the normal time and the diagnosis time of the diagnostic target device calculated by the probability density function calculating means, A symmetric cullback information amount calculating means for calculating a symmetric cullback information amount ID defined in the above, and comparing the calculated symmetric cullback information amount ID with a predetermined threshold value to determine whether or not the diagnosis target device is abnormal It is characterized by having a determination means.

(2) Further, in the device described in (1) above, the waveform sampling means has a function of calculating a Lissajous waveform by synthesizing two time waveforms in the X and Y directions of vibration in the sampled waveform. It is characterized by being.

(3) Further, in the above-described (1), the waveform sampling means has a function of converting the acquired waveform into an autocorrelation function waveform.

(4) Further, in the device described in (1), the waveform sampling means has a function of converting the sampled waveform into an envelope waveform.

(5) Further, in any of the above (1) to (4), the sensor is a vibration sensor, an acoustic sensor, an AE sensor, a displacement sensor, a strain sensor, a pressure sensor, a current sensor, a voltage sensor, One of the power sensors or a plurality of sensors selected from these sensors.

In the present invention, since the optimum sampling condition is set for each equipment device in which the sensor is installed as a step before performing the analysis by the diagnostic processing means, the diagnostic processing means used for the diagnosis target and analysis Optimum data sampling can be performed, and therefore highly reliable equipment diagnosis can be realized.
In addition, since the symmetrical cullback information amount is used for the diagnostic processing, accurate diagnosis is also made for abnormality diagnosis of equipment devices such as plunger type pump intake and discharge valves that are difficult to generate vibration waveforms during normal operation. be able to.

It is a block diagram of a diagnostic apparatus for equipment according to an embodiment of the present invention. It is explanatory drawing of the whole structure of the diagnostic apparatus of the equipment apparatus which concerns on one embodiment of this invention. It is explanatory drawing explaining the effect of this invention, and is a tendency management graph in the case where a symmetrical cullback information amount (ID value) is used, and the case where the conventional cullback information amount (KL value) is used. It is explanatory drawing of Embodiment 2 of this invention. It is explanatory drawing of Embodiment 2 of this invention. It is explanatory drawing of Embodiment 3 of this invention. It is explanatory drawing of Embodiment 3 of this invention. It is explanatory drawing of Embodiment 4 of this invention. It is explanatory drawing of Embodiment 4 of this invention. It is explanatory drawing explaining the subject which this invention tends to solve. It is explanatory drawing explaining the subject which this invention tends to solve. It is explanatory drawing explaining the subject which this invention tends to solve. It is explanatory drawing explaining the means for solving a subject.

A diagnostic apparatus for equipment according to an embodiment of the present invention will be described with reference to FIGS. 1 and 2.
In the present embodiment, a vibration sensor is described as an example of the sensor. However, the present invention is not limited to the vibration sensor, and in addition to the vibration sensor, an acoustic sensor, an AE (Acoustic Emission) sensor, a displacement sensor, and a strain sensor. , A pressure sensor, a current sensor, a voltage sensor, a power sensor, or a plurality of sensors selected from these sensors can be used.

As shown in FIG. 2, the equipment diagnosis apparatus 1 according to the present embodiment receives vibration signals from a vibration sensor 5 installed in, for example, a plunger pump 3 that is a diagnosis target, and collects vibration data. The data transmitted from the data collection device 7 by connecting the collection device 7, the hub 9 connecting the plurality of data collection devices 7 to the LAN, and instructing the sampling time and sampling frequency to the data collection device 7 connected to the LAN. PC 11 (personal computer) for monitoring which performs abnormality diagnosis of equipment is provided.
Each configuration will be described in detail.

<Data collection device>
As shown in FIG. 1, the data collection device 7 receives signals output from the plurality of vibration sensors 5 and receives vibration waveform data for each vibration sensor 5, and the analog circuit 13 receives the analog waveform 13. An A / D conversion circuit 15 that converts data into digital data, and a sampling processing circuit 17 that samples the data digitally converted by the A / D conversion circuit 15 based on the setting of the sampling condition setting means 19 and sends the data to the storage means 21 A sampling condition setting means 19 for setting a sampling condition of data in the sampling processing circuit 17, a storage means 21 for storing data sent by the sampling processing circuit 17, and a data for monitoring by reading out the data accumulated in the storage means 21. Data transmitting means 23 for transmitting to the PC 11 and the communication line And a communication control unit 25 for communicating with continued surveillance for PC 11.

The analog circuit 13 includes an integration circuit, a high pass filter, a low pass filter, and the like.
The analog circuit 13, the A / D conversion circuit 15, and the sampling processing circuit 17 constitute the vibration waveform capturing circuit of the present invention.
The sampling condition setting means 19 sets the sampling time and sampling frequency of the vibration data input from the vibration sensor 5 in accordance with an instruction from the monitoring PC 11, and sets the vibration sensor 5 to be simultaneously sampled among the plurality of vibration sensors 5. Further, the filter switching is set for which of the high-pass filter and the low-pass filter provided in the analog circuit 13 is allowed to pass.
The sampling time set by the sampling condition setting means 19 is, for example, 1.0 to 30 seconds, and the sampling frequency is, for example, 0.1 kHz to 40 kHz.
The sampling condition setting means 19 and the data transmission means 23 are realized by the CPU executing a program.

  The data collection device 7 includes a communication control unit 25. The communication control unit 25 can receive an instruction regarding data sampling from the monitoring PC 11 and can transmit the collected data to the monitoring PC 11.

<Monitoring PC>
The monitoring PC 11 includes a communication control means 39 for communicating with the data collection device 7 connected via a communication line, a sampling condition input means 41 for inputting a sampling time and a sampling frequency of data to be sampled, a data collection From diagnosis processing means 43 for performing abnormality diagnosis processing using data transmitted from the apparatus 7, storage means 45 for storing data and diagnosis results transmitted from the data collection apparatus 7, a keyboard for an operator to input information, and the like Input means 47 and display means 49 including a monitor or the like.

The sampling condition input means 41 displays, for example, an input frame for inputting the sampling time, sampling frequency, simultaneous sampling target, and filter switching for each vibration sensor 5 of each data collection device 7 on the monitor. Configured to allow input.
The diagnostic processing means 43 calculates a probability density function of the waveform collected by the waveform collecting means 43a and the waveform collecting means 43a for collecting the waveform in the operating state of the diagnosis target device based on the data sampled by the data collecting device. Probability density function calculation means 43b, and symmetric cullback for calculating a symmetric cullback information amount defined by the following expression from the probability density functions of the diagnosis target device calculated by the probability density function calculation means 43b at the normal time and the diagnosis time An information amount calculation unit 43c and a determination unit 43d that compares the calculated symmetrical cullback information amount with a predetermined threshold value to determine whether or not the diagnosis target device is abnormal are provided.
The diagnosis processing unit 43 is realized by executing a program such as an analysis program by the CPU.

A method for performing online monitoring of equipment according to the present embodiment configured as described above will be described.
First, it is necessary to set the optimum sampling time and sampling frequency for the device to be diagnosed. Therefore, the operator displays the sampling condition input screen on the display means 49, and sets the sampling time, sampling frequency, simultaneous sampling target sensor, etc. for each vibration sensor 5 of each data collection device 7 displayed on the screen by the input means 47. input.

Although different from the example shown in FIG. 2, for example, in the case where the vibration sensor 5 is installed in the bearing box of the motor and the output box of the reducer connected to the motor, For the vibration sensor 5 of the motor bearing box, the sampling time is set short and the sampling frequency is set high, and for the vibration sensor 5 on the reduction gear side, the sampling time is set long and the sampling frequency is set low.
When the operator inputs necessary information and gives a transmission instruction, the input data is transmitted to each data collection device 7 by the communication control means 39 via the communication line.

In each data collection device 7, based on the sampling condition transmitted from the monitoring PC 11, the sampling condition setting means 19 sets the sampling conditions such as the sampling time, sampling frequency, and simultaneous sampling target for each vibration sensor 5 in the sampling processing circuit 17. To instruct. As a result, in the data collection device 7, data based on the set conditions such as the set sampling time, sampling frequency, and presence / absence of simultaneous sampling is stored in the storage unit 21. That is, optimal data sampling is possible for each facility device in which each vibration sensor 5 is installed.
The above is the initial setting.

When the setting of the sampling conditions is completed as described above, the vibration waveform data in the normal state of the equipment to be diagnosed is collected by the waveform collection means 43a. Specifically, when the operator instructs the start of diagnosis from the input means 47, the analysis program is executed and the diagnosis processing means 43 is activated. At this time, the vibration waveform data transmitted from the data collection device 7 is stored in the storage means 45 via the communication control means 39, and the vibration waveform data stored in the storage means 45 is used for the diagnosis process.
The vibration waveform data is preferably normalized by the rms value of its own waveform and made dimensionless. This is because the overall vibration level varies greatly depending on the load condition, and by normalizing with the rms value, the waveform shape can always be captured on the same scale.
The probability density function calculating unit 43b calculates the probability density function from the vibration waveform data and stores the distribution graph in the storage unit 45.
The processing so far corresponds to pre-processing performed before actual diagnosis, and the following processing corresponds to actual diagnosis processing.

In the normal operation state of the equipment, the waveform collection means 43a collects vibration waveform data, the probability density function calculation means 43b calculates the probability density function from the collected vibration waveform data, and the distribution graph is stored in the storage means 45. . The waveform collected here is normalized by the rms value of the vibration waveform in the normal state as in the case of the preprocessing.
From both the distribution graph calculated in the preprocessing and the distribution graph at the time of diagnosis, the symmetric cullback information amount calculation means 43c calculates the symmetric cullback information amount defined by the following equation (2).

  The determination unit 43d compares the ID value with a predetermined threshold value and performs a determination process. At this time, if an abnormal value is diagnosed, an alarm is output. The analysis results are saved as trend management data.

The flow from preprocessing to diagnosis described above is summarized as follows.
(i) Collect vibration waveform data under normal conditions.
At this time, it is normalized by the rms value of its own waveform and made dimensionless.
(ii) A probability density function is calculated from the above vibration waveform data, and the distribution graph is stored.
(iii) Collect vibration waveform data in the operating state to be diagnosed.
(iv) A probability density function is calculated from the vibration waveform data and the distribution graph is stored.
The waveform collected here is normalized by the rms value of the vibration waveform in (i) as in (i).
(v) The symmetric cullback information amount is calculated from both the distribution graph (ii) and the distribution graph (iV).
(vi) The ID value is compared with a predetermined threshold value and a determination process is performed.
(vii) Repeat steps (iii) to (vi) for trend management.

As described above, in the present embodiment, the optimum sampling condition is set for each facility device in which the sensor 5 is installed from the monitoring PC 11 as a stage before analysis by the diagnostic processing means 43. Therefore, it is possible to sample optimal data for the diagnostic processing means 43 used for the diagnosis object and analysis, and therefore, a highly reliable facility diagnosis can be realized.
Further, in the present embodiment, since the symmetric cullback information amount is used, it is also suitable for abnormality diagnosis of an equipment device that does not easily generate a vibration waveform during normal operation, for example, an intake / discharge valve of a plunger type pump. Diagnosis can be made.
Furthermore, as described above, since the data to be used is data sampled at an appropriate sampling time and at an appropriate sampling frequency for each facility device, the analysis based on the symmetric cullback information amount can be effectively performed. Can be diagnosed accurately.

Hereinafter, the comparison between the case of using the symmetric cullback information amount and the case of using the normal cullback information amount will be described.
[Equipment specifications]
Equipment to be diagnosed: Plunger type pump intake valve Crankshaft rotation speed: 276rpm
Sampling data: ACC waveform data

[Diagnosis example]
FIG. 3 shows a graph when the diagnosis target equipment is monitored online. The vertical axis indicates the amplitude cullback information amount, and the horizontal axis indicates time. In the graph of FIG. 3, the black squares indicate the symmetric cullback information amount (ID), and the white squares indicate the conventional cullback information amount (KL).
The values in the graph of FIG. 3 are read as follows.
(1) Normal: KL value = 0.003
(2) Abnormal: KL value = 0.12 (changes to 40 times normal)
(3) Normal: ID value = 0.003
(4) Abnormal: ID value = 0.29 (changes to 96 times normal)

From the above, it can be seen that the ID value has a higher rate of change than the KL value, that is, the ID value has higher detection sensitivity. Therefore, it was found that by adopting the ID value, the defect of the amount of the Cullback information can be compensated and the abnormality can be detected sensitively.
In addition, although the vertical axis | shaft of FIG. 3 has shown the amplitude cullback information amount, the vertical axis | shaft is normalized and shown by making the normal value (0.003) of ID value and KL value into a reference value (1.0), This is preferable because it makes it easier to see the abnormal values.

[Embodiment 2]
In the first embodiment, the probability density function is calculated from the time waveform. However, there are cases where it is difficult to distinguish between a normal state and an abnormal state with a time waveform such as a two-dimensional movement facility, for example, around the shaft.
Therefore, in this embodiment, a time waveform is converted into a Lissajous waveform, a probability density function is calculated based on the Lissajous waveform, and a symmetric cullback information amount is further calculated.

4 and 5 are explanatory diagrams of the present embodiment, and illustrate the conversion of the time waveform in the XY directions into a Lissajous waveform. FIG. 4 shows a normal waveform, and FIG. 5 shows an abnormal waveform.
4 and 5, (a) is a vibration waveform in the X direction, (b) is a vibration waveform in the Y direction, and (c) is a Lissajous waveform obtained by combining vibration waveforms in the X and Y directions. It is.
By comparing FIG. 4C and FIG. 5C, the abnormal state can be accurately grasped.

  Then, conversion to a Lissajous waveform is performed, a probability density function is calculated based on the Lissajous waveform, and a symmetric cullback information amount is further calculated, thereby enabling accurate diagnosis.

  The conversion to the Lissajous waveform as described above may be performed by adding an additional function to the waveform collection unit 43a in the diagnosis processing unit 43 described in the first embodiment.

[Embodiment 3]
In the present embodiment, a time waveform is converted into an autocorrelation function as preprocessing for calculating a probability density function.
The present embodiment is effective for diagnosing equipment having periodic motion such as a reciprocating rotary machine.

  FIG. 6 and FIG. 7 are explanatory diagrams of the present embodiment. FIG. 6 shows a normal waveform and FIG. 7 shows an abnormal waveform. 6 and 7, (a) shows the velocity time waveform, and (b) shows the waveform converted into the autocorrelation function. As can be seen from FIG. 6 and FIG. 7, by converting the autocorrelation function, the periodic vibration waveform of the facility to be diagnosed is clarified, and the diagnosis focusing on the periodic vibration waveform is performed. It becomes possible.

  After the conversion to the autocorrelation function waveform, the probability density function is calculated based on the autocorrelation function waveform, and the symmetric cullback information amount is further calculated, thereby enabling accurate diagnosis.

[Embodiment 4]
In the present embodiment, a time waveform is converted into an envelope waveform as preprocessing for calculating a probability density function.
As in the third embodiment, the present embodiment is effective for diagnosing equipment that moves periodically, such as a reciprocating rotary machine.

  FIG. 8 and FIG. 9 are explanatory diagrams of the present embodiment. FIG. 8 shows a normal waveform and FIG. 9 shows an abnormal waveform. 8 and 9, (a) shows an acceleration time waveform, and (b) shows an envelope waveform. As can be seen from FIG. 8 and FIG. 9, by converting to an envelope waveform, it is possible to make a diagnosis focusing on periodic vibrations of the diagnosis target equipment.

  By converting to an envelope waveform, calculating a probability density function based on the envelope waveform, and further calculating a symmetric cullback information amount, an accurate diagnosis can be made.

1 Diagnosis device 3 Plunger pump 5 Vibration sensor 7 Data collection device 9 Hub 11 Monitoring PC
DESCRIPTION OF SYMBOLS 13 Analog circuit 15 A / D conversion circuit 17 Sampling processing circuit 19 Sampling condition setting means 21 Storage means 23 Data transmission means 25 Communication control means 39 Communication control means 41 Sampling condition input means 43 Diagnosis processing means 43a Waveform collection means 43b Probability density function Calculation means 43c Symmetrical cullback information amount calculation means 43d Determination means 45 Storage means 47 Input means 49 Display means

Claims (4)

A data collection device that collects data by inputting a signal from a sensor installed in a device to be diagnosed, and a diagnostic device for equipment that includes a monitoring computer that transmits and receives data to and from the data collection device,
The data collection device includes a data acquisition circuit that inputs a signal output from a sensor and acquires data, and a sampling condition that inputs an instruction signal input from the outside and sets a data sampling condition for the data acquisition circuit Setting means and data transmission means for transmitting the sampled data to the outside,
According to the instruction signal, the sampling condition setting means is configured to pass a sampling time of data input from the sensor, a sampling frequency, a sensor that performs simultaneous sampling among a plurality of sensors, and which of a high-pass filter and a low-pass filter. Set filter switching,
The monitoring computer comprises sampling condition input means for inputting sampling conditions by the data collection device, and diagnostic processing means for diagnosing the state of equipment based on data transmitted from the data collection device,
The sampling condition input means is configured so that the operator can input the sampling time, sampling frequency, simultaneous sampling target, filter switching by the input means for each sensor of the data collection device,
The diagnostic processing means, wherein the data acquisition device based on the sampled data sampled waveforms in the operating state of the diagnostic object apparatus normalizes the rms value of its waveform, the vibration in the dimensionless phased waveform X, Waveform sampling means having a function of calculating a Lissajous waveform by synthesizing two time waveforms in the Y direction, and a probability density function calculation for calculating a probability density function based on the Lissajous waveform calculated by the waveform sampling means And a symmetric cullback information amount calculation for calculating a symmetric cullback information amount ID defined by the following equation from the probability density function at the normal time and diagnosis time of the diagnosis target device calculated by the probability density function calculating means: Means for determining whether the diagnosis target device is abnormal by comparing the calculated symmetrical cullback information amount ID with a predetermined threshold value. Diagnostic apparatus of the equipment which is characterized in that there was example.

A data collection device that collects data by inputting a signal from a sensor installed in a device to be diagnosed, and a diagnostic device for equipment that includes a monitoring computer that transmits and receives data to and from the data collection device,
The data collection device includes a data acquisition circuit that inputs a signal output from a sensor and acquires data, and a sampling condition that inputs an instruction signal input from the outside and sets a data sampling condition for the data acquisition circuit Setting means and data transmission means for transmitting the sampled data to the outside,
According to the instruction signal, the sampling condition setting means is configured to pass a sampling time of data input from the sensor, a sampling frequency, a sensor that performs simultaneous sampling among a plurality of sensors, and which of a high-pass filter and a low-pass filter. Set filter switching,
The monitoring computer comprises sampling condition input means for inputting sampling conditions by the data collection device, and diagnostic processing means for diagnosing the state of equipment based on data transmitted from the data collection device,
The sampling condition input means is configured so that the operator can input the sampling time, sampling frequency, simultaneous sampling target, filter switching by the input means for each sensor of the data collection device,
The diagnostic processing means, wherein the data acquisition device based on the sampled data sampled waveforms in the operating state of the diagnostic object apparatus normalizes the rms value of its waveform, the dimensionless phased waveform autocorrelation function waveform A waveform sampling means having a function of converting into a probability density function calculating means for calculating a probability density function based on the autocorrelation function waveform converted by the waveform sampling means, and calculating by the probability density function calculating means A symmetric cullback information amount calculating means for calculating a symmetric cullback information amount ID defined by the following equation from a probability density function at the time of normality and diagnosis of the device to be diagnosed, and a calculated symmetric cullback information amount A facility device diagnostic apparatus comprising: a determination unit that compares an ID with a predetermined threshold to determine whether the diagnosis target device is abnormal.

A data collection device that collects data by inputting a signal from a sensor installed in a device to be diagnosed, and a diagnostic device for equipment that includes a monitoring computer that transmits and receives data to and from the data collection device,
The data collection device includes a data acquisition circuit that inputs a signal output from a sensor and acquires data, and a sampling condition that inputs an instruction signal input from the outside and sets a data sampling condition for the data acquisition circuit Setting means and data transmission means for transmitting the sampled data to the outside,
According to the instruction signal, the sampling condition setting means is configured to pass a sampling time of data input from the sensor, a sampling frequency, a sensor that performs simultaneous sampling among a plurality of sensors, and which of a high-pass filter and a low-pass filter. Set filter switching,
The monitoring computer comprises sampling condition input means for inputting sampling conditions by the data collection device, and diagnostic processing means for diagnosing the state of equipment based on data transmitted from the data collection device,
The sampling condition input means is configured so that the operator can input the sampling time, sampling frequency, simultaneous sampling target, filter switching by the input means for each sensor of the data collection device,
The diagnostic processing means converts the by the data collection device on the basis of the sampled data sampled waveforms in the operating state of the diagnostic object apparatus normalizes the rms value of its waveform, the dimensionless phased waveform envelope waveform A waveform collecting means having a function to perform, a probability density function calculating means for calculating a probability density function based on an envelope waveform converted by the waveform collecting means, and the diagnosis calculated by the probability density function calculating means A symmetric cullback information amount calculating means for calculating a symmetric cullback information amount ID defined by the following equation from a probability density function when the target device is normal and diagnosed, and a calculated symmetric cullback information amount ID are determined in advance. A device for determining whether or not the device to be diagnosed is abnormal as compared with a threshold value.

The sensor is a vibration sensor, an acoustic sensor, an AE sensor, a displacement sensor, a strain sensor, a pressure sensor, a current sensor, a voltage sensor, or a power sensor, or a plurality of sensors selected from these sensors. The diagnostic apparatus for equipment according to any one of claims 1 to 3.

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