JP2010175446A - Status diagnostic apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a status diagnostics appartatus capable of detecting a sign of abnormality. <P>SOLUTION: The apparatus includes: a memory part 11 where two or more monitoring items for a device of a diagnosis object are classified and stored as main monitoring items and sub-monitoring items with a behavior changeable depending on the status of the main monitoring items; a measurement data collection part 12 where measurement data in the monitoring items of the diagnosis object are successively collected and stored; a data classification part 13 where measurement data in the sub-monitoring items are classified depending on the status of the main monitoring items when the data are taken; a normal range setting part 14 where a normal range of the sub-monitoring items is set per classification using the measurement data of the sub-monitoring items in each classification; and an evaluation part 15 where the status of the device is evaluated using the normal range of the sub-monitoring items in each classification. In the normal range setting part 14, the normal range is updated as needed using the measurement data stored from now to a past predetermined period among the measurement data stored successively to the measurement data collection part 12. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a state diagnosis apparatus.

  For example, as a method for determining an abnormal state of a device, a method is generally known in which a threshold value for determining that an abnormality has occurred is set based on device specifications and the abnormality is notified when the threshold value is exceeded. For example, in detecting an abnormality of a main engine turbine for a ship, threshold values for determining an abnormality are set in advance for various factors such as a bearing temperature and a vibration value for determining the state of the main engine turbine, each part pressure, and each part temperature. In addition, when each factor exceeds the respective threshold value, a method is adopted in which it is determined that an abnormality has occurred and the worker is notified accordingly.

JP-A-8-220278

  In the conventional abnormality detection method as described above, since only a comparison between the threshold value and each measured value is performed, it is impossible to grasp a state change during the change from the normal state to the abnormal state. For this reason, it was impossible to detect signs of abnormality in advance and take measures before the abnormality occurred.

  This invention is made | formed in view of such a situation, Comprising: It aims at providing the state diagnostic apparatus which can detect the sign of abnormality.

In order to solve the above problems, the present invention employs the following means.
The present invention relates to a storage means in which a plurality of monitoring items for a device to be diagnosed are stored by being divided into a main monitoring item and a sub-monitoring item whose behavior changes according to the state of the main monitoring item, Measurement data collection means for sequentially collecting and accumulating measurement data in each monitoring item to be diagnosed, and measurement data for each sub-monitoring item collected by the measurement data collection means, when the measurement data is acquired Data classification means for classifying according to the state of the main monitoring item, normal range setting means for setting the normal range of the monitoring item for each classification using the measurement data of the monitoring item in each classification, and the normal Evaluation means for evaluating the state of the device using the normal range of the sub-monitoring item in each category set by the determination setting means, and the normal range setting means sequentially sends the measurement data collection means Of the measured data to be a product, provide status diagnostic device from time to time updating the normal range by using the measurement data accumulated in the past predetermined period from the present.

In the present invention, the measurement data collecting means sequentially collects and accumulates measurement data relating to each main monitoring item and each sub monitoring item of the diagnosis target device. Of the collected measurement data, the measurement data of the secondary monitoring items is classified by the data classification means according to the status of the primary monitoring items, and the normal range of the monitoring items is set by the normal range setting means for each classification. The Then, using the normal range set by the normal range setting unit, the state of the device is evaluated by the evaluation unit. In this case, the normal range set by the normality determination setting means is constantly updated in a form that takes into account new measurement data, so the normal operating range of the device at that time, in other words, a normal range that takes into account aged deterioration, etc. Can be set. Thus, the device can be diagnosed using an appropriate normal range according to the state of the device to be diagnosed. In addition, since the normal range is set using measurement data actually acquired from the device to be diagnosed, it is possible to set a normal range more suitable for the device.
Thus, according to the present invention, since the diagnosis of the device is performed using the normal range that dynamically changes in time series, the change in the state of the device can be followed and the change from the normal state to the abnormal state. You can grasp the progress. As a result, an abnormality sign can be detected, and an appropriate measure can be taken before the abnormality occurs.
Further, according to the present invention, the measurement data of the sub-monitoring items is classified according to the state of the main monitoring item, and the normal range is set for each category, so that the device diagnosis can be performed in more detail. Thereby, it becomes possible to evaluate the state of an apparatus more appropriately.

  In the above-described state diagnosis apparatus, the normal range setting means may set the normal range from the average value and standard deviation of the measurement data in each section.

  In the state diagnosis apparatus, the evaluation means compares the measurement data of the current sub-monitoring item with the normal range of the category to which the measurement data corresponds, and if the measurement data is outside the normal range, an abnormality occurs. It may be determined that there is a sign of this.

  In the state diagnosis apparatus, the evaluation unit may evaluate the state of the device based on a transition of a normal range updated as needed by the normal range setting unit.

  Since the state of the device is evaluated based on the transition state of the normal range, the state of the device to be diagnosed can be grasped in more detail.

  In the state diagnosis apparatus, when the diagnosis target is a marine main engine turbine, the plurality of monitoring items are, for example, main shaft rotation speed, main engine output, bearing temperature, vibration, steam temperature, and pressure. The monitoring items are, for example, the main shaft rotation speed and the main engine output, and the sub monitoring items are, for example, bearing temperature, turbine vibration, turbine shaft position, steam temperature, and pressure.

  According to the present invention, since an abnormality sign is detected, the occurrence of abnormality can be prevented in advance, and effective measures for avoiding abnormality can be taken before the abnormality occurs.

It is the block diagram which showed schematic structure of the state diagnostic apparatus which concerns on one Embodiment of this invention. It is the flowchart which showed the operation | movement content of the state diagnostic apparatus which concerns on one Embodiment of this invention. It is the figure which showed an example of the normal range of the bearing temperature when the main monitoring item is a spindle speed. It is the figure which showed an example of the normal range of the bearing temperature when the main monitoring item is set to the main engine output. It is the figure which showed the example in the case of determining the abnormality sign based on the change of a normal range.

  Embodiments according to the state diagnosis apparatus of the present invention will be described below with reference to the drawings. In the following embodiments, the case where the state diagnosis device of the present invention is applied to the state diagnosis of a marine main engine turbine will be described.

  FIG. 1 is a block diagram showing a schematic configuration of a state diagnosis apparatus according to an embodiment of the present invention. As shown in FIG. 1, the state diagnosis apparatus 1 includes a storage unit (storage unit) 11, a measurement data collection unit (measurement data collection unit) 12, a data division unit (data division unit) 13, and a normal range setting unit. (Normal range setting means) 14 and an evaluation unit (evaluation means) 15 are provided.

  The storage unit 11 stores a plurality of monitoring items for the marine main engine turbine to be diagnosed, divided into a main monitoring item and a sub-monitoring item whose behavior changes according to the state of the main monitoring item. . In the present embodiment, spindle speed, main engine output, bearing temperature, turbine vibration, turbine shaft position, steam temperature, and pressure are registered as monitoring items, and main spindle speed and main engine output are registered as main monitoring items. As subordinate monitoring items, bearing temperature, vibration, steam temperature, and pressure are registered.

  Measurement data is transmitted to the measurement data collection unit 12 in real time from sensors attached to each part of the marine main engine turbine. As a result, the measurement data collection unit 12 sequentially accumulates measurement data for each monitoring item, that is, measurement data of the main shaft rotation speed, main engine output, bearing temperature, turbine vibration, turbine shaft position, steam temperature, and pressure.

  The data sorting unit 13 refers to the information about the main monitoring item and the sub monitoring item stored in the storage unit 11 and uses the measurement data of each sub monitoring item stored in the measurement data collecting unit 12 as the measurement data. Is classified according to the state of the main monitoring item when the is acquired. For example, when the spindle speed is assumed as the main monitoring item, the range of the spindle speed in the normal state, for example, the range from the minimum speed to the maximum speed is divided in increments of a predetermined speed, and based on this classification Classify the monitoring item measurement data. In the present embodiment, the rotation speed range from 20 rpm to the maximum rotation speed x 1.05 is divided in increments of 1 rmp.

  In addition, when the main engine output is assumed as the main monitoring item, the range of the main engine output in the normal state, for example, the minimum output to the maximum output is divided in predetermined output increments, and the measurement data of the sub monitoring item is based on this division Is divided. In this embodiment, the output range from 500 kW to the maximum output × 1.1 is divided in increments of 250 kW.

  The normal range setting unit 14 sets the normal range of the monitoring item for each category using the measurement data of the monitoring item for each category. Specifically, the normal range setting unit 14 sets a normal range from the average value and standard deviation of measurement data in each section. For example, the normal range setting unit 14 has a coefficient α corresponding to each slave monitoring item, and a value obtained by subtracting a value obtained by multiplying the coefficient α by the standard deviation σ from the average value μ is set as a lower limit value of the normal range. A value obtained by multiplying the coefficient α by the standard deviation σ and the average value μ is set as the upper limit value of the normal range.

The normal range is expressed as follows.
Lower limit: μ-α * σ
Upper limit: μ + α * σ
Here, the average value μ and the standard deviation σ are expressed by the following equations (1) and (2), respectively.

In the above expressions (1) and (2), T _k (k = 1, 2,..., N) is measurement data in a certain section, and n is the number of measurement data in a certain section.

  In addition, the normal range setting unit 14 updates the normal range at predetermined time intervals using the measurement data in the past predetermined period from the current time collected by the measurement data collecting unit 12. As a result, the normal range is updated from time to time in a form that always incorporates new measurement data.

  The evaluation unit 15 evaluates the state of the device using the normal range of the slave monitoring item in each category set by the normal range setting unit 14. Specifically, the evaluation unit 15 compares the measurement data of the current slave monitoring item with the normal range of the category to which the measurement data corresponds, and if the current measurement data is outside the normal range, If a sign is detected, the operator is notified accordingly.

Next, the operation of the state diagnosis apparatus 1 according to the present embodiment having the above configuration will be described with reference to FIG.
First, measurement data detected by the main machine or various sensors attached to the periphery of the main machine is collected by the measurement data collecting unit 12 (step SA1 in FIG. 2). Each measurement data collected in this manner is stored in the measurement data collection unit 12 for each monitoring item in association with time information when the measurement data is measured.

Subsequently, in the data sorting unit 13, the measurement data for each monitoring item collected by the measurement data collecting unit 12 is read, and the measurement data of the sub-monitoring items therein is sorted according to the state of the main monitoring item. (Step SA2 in FIG. 2).
For example, when the spindle speed is assumed as the main monitoring item, the rotation speed range from 20 rpm to the maximum number of revolutions × 1.05 is divided in increments of 1 rmp, and the measurement data of each slave monitoring item is based on this classification. Break down. Thereby, for example, if the bearing temperature, the monitoring data group of the bearing temperature measured at the spindle rotation speed of 20 rpm, the measurement data group of the bearing temperature measured at the spindle rotation speed of 21 rpm, etc. Item measurement data is classified.

When the main machine output is assumed as the main monitoring item, the output range from 500 kW to the maximum output × 1.1 is divided in increments of 250 kW, and the measurement data of each sub monitoring item is divided based on this division. Thereby, for example, if the bearing temperature, the bearing data measurement data group measured when the main engine output is 500 kW or more and less than 750 kW, the bearing temperature measurement data group measured when the main machine output is 750 kW or more and less than 1000 kW, etc. The measurement data of the subordinate monitoring items is divided into the following.
Since each measurement data stored in the measurement data collection unit 12 is associated with time information when the measurement data is measured, the measurement data of each sub-monitoring item is stored using this time information as a key. It is possible to grasp the state of the main monitoring item when acquired.

Subsequently, the normal range setting unit 14 calculates the average value and standard deviation of the measurement data for each category, and sets the normal range for each category using these values (step SA3 in FIG. 2). As a result, the normal range is set for each category for each sub-monitoring item.
FIG. 3 shows an example of a normal range of the bearing temperature with respect to the main shaft rotation speed, and FIG. 4 shows an example of a normal range of the bearing temperature with respect to the main engine output. In FIG. 3, the horizontal axis indicates the main shaft rotation speed, and the vertical axis indicates the bearing temperature. In FIG. 4, the horizontal axis indicates the main engine output, and the vertical axis indicates the bearing temperature. 3 and 4 show the case where the coefficient α is set to 1.5. In FIG. 4, the normal range is set continuously by connecting the normal range calculated in increments of 250 kW so as to form a smooth curve. Furthermore, in FIG.3 and FIG.4, the alarm setting value conventionally used for abnormality determination is shown. If the bearing temperature exceeds this alarm set value, it is determined that an abnormality has actually occurred.

  The evaluation unit 15 evaluates the state of the device by comparing the measurement data of each slave monitoring item collected by the measurement data collection unit 12 with the normal range set by the normal range setting unit 14 (step of FIG. 2). SA4). For example, when the state of the main monitoring item when the current bearing temperature T ′ is acquired is the main spindle rotational speed N and the main engine output Q, the evaluation unit 15 determines the normal range corresponding to the main spindle rotational speed N. Is compared with the bearing temperature T ′, and the normal range corresponding to the main engine output Q is compared with the bearing temperature T ′. When the bearing temperature T ′ deviates from at least one of the normal ranges, it is determined that there is a sign of abnormality and a notification to that effect is given.

  The normal range is updated by the normal range setting unit 14 as needed by repeatedly performing the processing from step SA1 to step SA4, and the evaluation of the device state based on the updated normal range is performed by the evaluation unit 15. .

  The time interval for updating the normal range by the normal range setting unit 14 may be different from the time interval for state evaluation by the evaluation unit 15 or may be the same. However, since it is usually difficult to think that the measurement data in each monitoring item changes suddenly in a normal state, it is preferable to set the time interval for updating the normal range sufficiently longer than the time interval for state evaluation. . By doing so, it is not necessary to update the normal range frequently, so that the processing load can be reduced (for example, the initial setting holds data for three years by sampling at intervals of 30 seconds).

As described above, according to the state diagnosis device 1 according to the present embodiment, the normal range setting unit 14 uses the measurement data in the past predetermined period collected from the present time collected by the measurement data collection unit 12 to change the normal range as needed. Since it is updated, it is possible to set a normal range that takes into account the operating state of the device to be diagnosed from time to time, in other words, aged deterioration and the like. Thereby, a device can be diagnosed using an appropriate normal range according to the state of the device to be diagnosed.
In addition, since the normal range is set using measurement data actually acquired from the diagnosis target device, it is possible to set a normal range more suitable for the device.
Thus, according to the present invention, since the diagnosis of the device is performed using the normal range that dynamically changes in time series, the change in the state of the device can be followed and the change from the normal state to the abnormal state. You can grasp the progress. Thereby, an abnormality sign can be detected, and an appropriate measure can be taken before the abnormality occurs.
Furthermore, since the measurement data of the sub-monitoring item is classified according to the state of the main monitoring item, and the normal range is set for each category, the device diagnosis can be performed in more detail. This makes it possible to more accurately evaluate the state of the device.

In addition, instead of or in addition to the evaluation method described in the above-described embodiment, the evaluation unit 15 determines an abnormality sign based on a change in the normal range that is updated as needed by the normal range setting unit 14. It is good.
For example, when the normal range updated at any time by the normal range setting unit 14 is compared in time series, and the tendency of the normal range set up to now and the tendency of the normal range set this time are significantly different It is good also as detecting the sign of abnormality.

More specifically, the latest normal range in at least one main monitoring item among various main monitoring items (for example, main engine speed, main engine output, etc.) is higher than the normal range collected in the past. Or if it is shifted downward by more than a predetermined amount, in other words, if there is a divergence of more than a predetermined amount between the latest normal range and the normal range collected in the past, It may be determined that a change in device behavior has occurred and an abnormal sign may be detected (see FIG. 5).
The above-mentioned diverging deviation not less than a predetermined amount means, for example, a case where the degree of deviation increases as the rotational speed increases, or a case where the degree of deviation increases as the main engine output increases.
In addition, when the latest normal range in a plurality of main monitoring items (for example, main monitoring items related to each other) is deviated by a predetermined amount or more compared to the normal range collected in the past, an abnormal sign is detected. It is good.

  In this way, not only can the normal range and current measurement data be compared to determine the sign of abnormality, but also by determining the sign of abnormality based on the transition state of the normal range, the device to be diagnosed can be examined in more detail. The state can be grasped.

In the above-described embodiment, the state diagnosis apparatus 1 is premised on processing by hardware, but is not necessarily limited to such a configuration. For example, a configuration in which processing is performed separately by software is also possible. In this case, the state diagnosis apparatus 1 includes a main storage device such as a CPU and a RAM, and a computer-readable recording medium on which a program for realizing all or part of the above processing is recorded. The CPU reads out the program recorded in the storage medium and executes information processing / calculation processing, thereby realizing processing similar to that of the above-described state diagnosis apparatus.
Here, the computer-readable recording medium means a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like.

  In the above-described embodiment, the case where the state diagnosis device of the present invention is applied to a marine main machine turbine has been described. However, the state diagnosis device of the present invention is not limited to a marine main machine turbine, and is a general electric such as a TV. It can be widely applied to parts in products, automobiles, etc., and is effectively used for predicting the remaining life of these parts and pre-extraction of occurrence of abnormalities.

DESCRIPTION OF SYMBOLS 1 State diagnostic apparatus 11 Storage part 12 Measurement data collection part 13 Data division part 14 Normal range setting part 15 Evaluation part

Claims (5)

A storage means for storing a plurality of monitoring items for a device to be diagnosed divided into a primary monitoring item and a secondary monitoring item whose behavior changes according to the state of the primary monitoring item;
Measurement data collection means for sequentially collecting and storing measurement data in each monitoring item to be diagnosed;
Data classification means for classifying the measurement data of each sub-monitoring item collected by the measurement data collecting means according to the state of the main monitoring item when the measurement data is acquired;
Normal range setting means for setting the normal range of the subordinate monitoring item for each category, using the measurement data of the subordinate monitoring item in each category,
Evaluation means for evaluating the state of the device using the normal range of the subordinate monitoring items in each category set by the normality determination setting means,
The normal range setting unit is a state diagnosis device that updates the normal range at any time using measurement data accumulated in the past predetermined period from the present among the measurement data sequentially accumulated in the measurement data collection unit.   The state diagnosis apparatus according to claim 1, wherein the normal range setting means sets a normal range from an average value and standard deviation of measurement data in each section.   The evaluation means compares the measurement data of the current sub monitoring item with the normal range of the category to which the measurement data corresponds, and determines that there is an abnormality sign when the measurement data is outside the normal range The state diagnosis apparatus according to claim 1 or 2.   The state diagnosis apparatus according to claim 1, wherein the evaluation unit evaluates the state of the device based on a transition of a normal range updated as needed by the normal range setting unit. In the case where the diagnosis target is a marine main engine turbine,
The plurality of monitoring items are spindle speed, main engine output, bearing temperature, vibration, steam temperature, pressure,
5. The state diagnosis apparatus according to claim 1, wherein the main monitoring items are a main shaft rotational speed and a main engine output, and the sub monitoring items are a bearing temperature, vibration, steam temperature, and pressure.

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