JP2010139248A - Method for predicting life of objective facility, computer program, and device for predicting life of objective facility - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a supporting method for determining an optimal maintenance time by predicting a life of an objective facility, and to provide a computer program, and a supporting device that predicts a life of an objective facility so as to determine an optimal maintenance time thereof. <P>SOLUTION: This supporting method includes a step 1 of estimating the number of repeating times of application of fluctuation stress at a bearing part on the basis of a time change of a power or a current of an electric motor in an objective facility and obtaining a consumption rate of a life so as to determine a remaining life, a step 2 of tracing a progress of a deteriorating condition by an exponentiation rule of a time change rate of a vibration speed obtained from a vibration sensor attached to the bearing part so as to estimate a time point of reaching a dangerous region, and a step 3 of determining a remaining life in accordance with the step 1 and step 2 in a late stage of a caution region on the basis of a result of vibration diagnosis in the step 2. In the step 3, the life of the objective facility is determined on the basis of the two time periods of the remaining life of the step 1 and step 2. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a method for predicting the lifetime of a target facility, a computer program, and an apparatus for predicting the lifetime of the target facility.

By acquiring vibration data of the target equipment, diagnosing the mechanical soundness of the target equipment, monitoring the tendency of the deterioration state, and predicting the lifetime, the maintenance time of the target equipment is determined.
For example, as the vibration data, data such as vibration displacement, vibration speed, vibration acceleration, vibration acceleration distortion, vibration acceleration kurtosis, vibration acceleration crest factor, and the like are acquired, and the acquired data is set as a predetermined threshold value. By comparing, it is determined whether or not there is an abnormality in the target equipment, and the state prediction is conventionally performed on the assumption that the data in a normal state follows a “normal distribution” (see, for example, Patent Document 1).

Alternatively, in order to solve the above-described problem of Patent Document 1, state determination and state prediction are performed using various information processing techniques by converting vibration waveform data and vibration feature values into a specified probability distribution, usually a normal distribution. (For example, refer to Patent Document 2).
Patent Publication 2000-171291 Patent WO2004-068088

In diagnosis and maintenance of the target equipment, the user predicts the life of the target parts and devices not only by detecting abnormalities early but also by trend monitoring, but the accuracy of the trend monitoring and life prediction is insufficient. Yes, it does not have objective criteria for determining when to actually stop the target equipment and recover the deteriorated part. The main reason is that in the conventional technology, the future is predicted from the past transition of the diagnostic index based on the vibration data in the normal state, but when the deterioration phenomenon occurs, the change in the index of the vibration data is past normal. This is because the state transition is completely different.
In addition, when the temporal fluctuation of the operating load of the target equipment is large, even if the applied stress acting on the rotating shaft and the bearing part is smaller than the design stress, the number of repetitions increases, and so-called metal fatigue shortens the life. Therefore, it was difficult to easily estimate the life consumption due to this fatigue.
Furthermore, in the target equipment where the operating load greatly fluctuates as described above, even if vibration data is collected during the time period when the operating load fluctuates, the vibration energy due to the degradation event of the diagnosis target part is caused by the driving fluctuation. The accuracy of early detection of minute anomalies, trend monitoring, and life prediction was not practical.

Originally, in order to determine the service life at the component level of the target equipment, the stress generated in the component or member is external force from three directions, frictional force, etc., and chemical damage (corrosion, etc.) of the component or member. Complex factors such as design / manufacturing specifications, product quality, career after operation, and changes in operating conditions in the future are intertwined, and it is necessary to examine them from various perspectives.
Here, taking up the factors from various aspects described above and accurately predicting the progress of the deterioration state and the life of the target part or member is the best measure, but it is not realistic. That being said, the transition of the degradation state with completely different progress mechanism is predicted by the progress, accumulation and analysis of vibration data or its feature value in the normal state where no degradation (abnormal) event has occurred as in the prior art. This method is not practical because it has little evidence.
In addition, if a repeated load is applied to the metal part of the target equipment, even if the applied load is smaller than the design stress, damage will occur from the fatigue phenomenon depending on the number of repetitions. Not done.

The present invention has been made in view of such a problem, and its purpose is to provide a method for predicting a deterioration state that is simple, realistic, and can be put to practical use, although it is a suboptimal measure. is there. In other words, this is a technique characterized by the following.
・ Search for more universal indicators that correspond to the type of abnormality deterioration ・ Search for indicators that correspond monotonously to the degree of deterioration of the abnormality ・ Construction of the effect of vibration speed on the deterioration progress of the abnormality from actual vibration data ・Estimate fluctuating stress of metal member from time change of motor current to predict metal fatigue life ・ Comprehensive evaluation of remaining life estimated from vibration data and result from life consumption estimated from motor current data

The main present invention is based on the result of diagnosing a plurality of deterioration states of the target equipment by vibration data and predicting the progress of the deterioration state and the life, and on the other hand, estimated from the temporal change of the current data of the driving motor. By analyzing the fluctuation loop of the applied stress, the life wear rate of the metal part of the target equipment is obtained, the remaining life is obtained, and the two results of life prediction obtained from the vibration data are comprehensively evaluated. It is in.
A simple diagnostic step for diagnosing mechanical soundness based on vibration data, a precise diagnostic step for estimating the type and degree of degradation of the mechanical degradation, a trend monitoring step for tracking the progress of the degradation state, and a trend monitoring step Vibration life prediction step that predicts the arrival time of the failure from the deterioration pattern of the failure and the current data of the drive motor of the target equipment, the fluctuation stress loop in the metal part is estimated and the part in the part due to metal fatigue A life prediction system for a target facility characterized by comprehensively evaluating a remaining life estimation step for obtaining a life wear rate and obtaining a remaining life and a prediction result in a life prediction step obtained from the vibration data described above. is there.

  Other features of the present invention will become apparent from the description of the present specification and the accompanying drawings.

  At least the following will be made clear by the description of the present specification and the accompanying drawings.

Multiple deterioration states of the target equipment are diagnosed by vibration data, the progress of the deterioration state is tracked and the life is predicted. On the other hand, the fluctuation loop of the applied stress estimated from the temporal change of the current data of the drive motor Is to comprehensively evaluate the two results of the remaining life obtained by obtaining the life wear rate of the metal part of the target facility and the vibration life prediction obtained from the vibration data described above.
A simple diagnostic step for diagnosing mechanical soundness based on vibration data, a precise diagnostic step for estimating the type and degree of degradation of the mechanical degradation, a trend monitoring step for tracking the progress of the degradation state, and a trend monitoring step Vibration life prediction step that predicts the arrival time of the failure from the deterioration pattern of the failure and the current data of the drive motor of the target equipment, the fluctuation stress loop in the metal part is estimated and the part in the part due to metal fatigue A life prediction system for a target facility that comprehensively evaluates a remaining life estimation step for obtaining a life wear rate and obtaining a remaining life and a prediction result in a vibration life prediction step obtained from the vibration data described above. .

  Two lifespans, such as diagnostic methods, trend monitoring and life prediction results based on vibration data of the target equipment, and results of remaining life predictions obtained by determining the life wear rate due to metal fatigue based on the current data of the drive motor By comprehensively evaluating from the prediction results, the life prediction of the target equipment becomes practical.

In the simple diagnosis step of the target equipment, a plurality of waveform feature amounts based on vibration time waveform data include vibration displacement, vibration speed, vibration acceleration, vibration acceleration skewness, vibration acceleration kurtosis, and vibration acceleration. Taking into account at least one of the crest factors of the noise and a plurality of deterioration-specific dimensionless feature quantities that are specific to the deterioration obtained by dividing the spectrum of the vibration data and correlating the strength between the divided frequency bands on the time axis It is good.
In such a case, when performing a simple diagnosis, a reliable diagnosis result is obtained not only by the determination result by the vibration waveform but also by the combined determination with the determination result by the dimensionless feature amount in the spectrum of the vibration data. You can also.

Also, the ISO absolute determination result based on the current vibration speed average value and the vibration displacement, vibration speed, vibration acceleration, vibration acceleration skewness, and vibration acceleration kurtosis, which are a plurality of waveform feature values in the previous period based on the time waveform data of vibration And a plurality of deteriorations specific to the deterioration obtained from the determination result based on at least one of the crest factors of vibration acceleration and the correlation of the strength of the divided frequency bands on the time axis by dividing the spectrum of the vibration data It is good also as determining the management treatment in the next time combining the determination result based on a specific dimensionless feature-value, and three types of determination results.
In such a case, it is possible to determine the type of treatment in the subsequent trend monitoring according to the degree of progress of deterioration of the target facility.

In addition, a plurality of deterioration-specific non-dimensional feature quantities that are specific to the deterioration obtained from the increase / decrease tendency of the average value of the current vibration speed and the correlation of the vibration data spectrum on the time axis of the strength between the divided frequency bands. It is good also as selecting the treatment classification of the monitoring level in the next time combining the increase / decrease tendency of the said degradation specific dimensionless feature-value corresponding to the said degradation event.
In such a case, it is possible to grasp the characteristics of the progress of the degradation state of the target equipment, the future prediction is simple, and the accuracy can be improved.

Alternatively, the deterioration state at the next time may be predicted based on the power law of the ratio between the average value of the vibration speed at the latest time and the average value of the vibration speed at the current time.
In such a case, since the influence of the vibration energy change at the deteriorated part due to the vibration speed is taken into account, the characteristics of the progress of cracks, wear, etc. at the deteriorated part and the accuracy of vibration life prediction will be improved. .

In addition, the fluctuation stress of the metal part of the target equipment is estimated from the time change of the current data of the drive motor from the start of operation of the target equipment to the stop of the operation, and the wear rate of the lifetime due to metal fatigue is obtained. The remaining life of the part may be estimated, and the vibration life prediction result obtained by tracking the deterioration state from the vibration data may be comprehensively analyzed and evaluated.
In such a case, it is possible to take into consideration the characteristics of fatigue events given to the metal part by the repeated load of the target equipment whose operating state and operating load vary greatly, and it is possible to improve the accuracy of life prediction.

Also, the vibration data spectrum is divided, and the deterioration-specific non-dimensional features corresponding to the deterioration event among a plurality of deterioration-specific non-dimensional features that are specific to the deterioration obtained from the correlation in the time axis of the strength between the divided frequency bands. For a dimension feature, a secondary caution threshold is provided between a primary caution threshold corresponding to the start of occurrence of the degradation event and a danger threshold for shifting from the caution area to the danger area, and the primary feature threshold The deterioration state may be predicted between the attention threshold and the second attention threshold.
In such a case, it is possible to greatly improve the prediction accuracy of the deterioration state while simply defining the period for extracting the progress characteristics related to the deterioration event.

It is also possible to implement a computer program for realizing the target equipment life prediction system.
According to such a computer program, it is easy to execute life prediction in consideration of progress of the deterioration event and metal fatigue of the deterioration portion in diagnosis, trend monitoring, and life prediction of the target equipment.

In addition, it is possible to realize an apparatus for predicting the life of the target equipment, characterized by having a computer equipped with the computer program.
According to the life prediction apparatus for the target facility, it is easy to perform life prediction in consideration of progress of the deterioration event and metal fatigue of the deterioration portion in diagnosis, trend monitoring, and life prediction of the target facility.

=== About Overall Flow of Life Prediction Method for Target Equipment According to this Embodiment ===
Fig. 1 shows the execution of simple diagnosis, precise diagnosis, trend monitoring, and life prediction based on vibration data, and the determination of the remaining life in consideration of metal fatigue by estimating the fluctuating stress of the diagnosis target part from the time change of the current data of the drive motor. FIG. 2 is a flowchart of each step for comprehensively analyzing and evaluating the life prediction result obtained from vibration data and the remaining life result obtained from current data. FIG. 2 is a comprehensive judgment based on each judgment result based on three judgment methods based on vibration data. A classification example of the future monitoring procedure is shown from the procedure and the comprehensive judgment result.

Collect vibration data of the target equipment, extract waveform feature values and deterioration-specific dimensionless feature values from the vibration data, determine the result of determination method 1 based on ISO absolute determination criteria, and the high frequency (Hi) and medium frequency (Mid ) ・ Determination method 2 based on waveform features at low frequency (Lo) and judgment in high frequency (Hi), medium frequency (Mid), and low frequency (Lo) regions based on degradation-specific dimensionless features Comprehensive determination with the determination result of method 3 is performed to determine the presence / absence of abnormality and a measure for future monitoring.
The determination result by the determination method 1 is a four-step determination of good / attention level 1 / attention level 2 / danger, and these are standardized by ISO10816-1 according to the size of the drive motor and the basic state of the target equipment. .

Next, in the determination method 2, the peak value, average value, mean square value, acceleration average value, square value of acceleration at the high frequency (Hi), medium frequency (Mid), and low frequency (Lo) in the decision method 1 described above. Dimensional feature quantities such as average values and peak values of displacement, and dimensionless feature quantities defined in the following [Equation 1] and [Equation 2], with each normal value as a reference, several times the reference value, Usually, 2 to 3 times is set as the caution threshold, and 2 to 3 times the caution threshold is set as the danger threshold.

  Next, in the determination method 3, the m-th principal component score is used as a determination index from the first principal component score obtained by Expression 4 disclosed in Japanese Patent No. 3382240. The above-mentioned principal component score is obtained for normal vibration data for each frequency region of high frequency (Hi), medium frequency (Mid), and low frequency (Lo), and the determination method using the principal component score as an index is described above. This is specifically described in FIG. 6 and paragraphs [0037] to [0042] in Japanese Patent No. 3382240. In this determination method 3, the primary caution threshold, the secondary caution threshold, and the danger threshold are set, and the four stages of determination of good / caution level 1 / caution level 2 and dangerous area are performed.

According to the result of method 1, that is, when the determination result of method 1 is good or attention level 1, Table 1 in FIG. 2 is applied, and when the determination result of method 1 is attention level 2 or a dangerous area. Comprehensive judgment as shown in Table 2 and subsequent monitoring treatment classification are performed.
That is, in Table 2, since the determination result of method 1 shows that there is a high probability that the average vibration speed value in the low frequency region is large and the deterioration tendency is serious, the overall determination of method 2 and method 3 and the subsequent monitoring procedure Compared to the case of Table 1, it is natural to ensure the safe operation by grasping the progress of deterioration of the target equipment by selecting strict monitoring measures, and the accuracy of life prediction by extracting the deterioration characteristics. It improves.
In the determination method described above, the frequency is divided into three frequency bands, high frequency (Hi), medium frequency (Mid), and low frequency (Lo), and the determination is performed in each band. Consider the fact that many initial phenomena of wear and scratches occur, that self-excited vibration is dominant in the middle frequency range, and that structural abnormalities such as unbalance mainly occur in the low frequency range. This improves the detection performance of the abnormality and is effective in identifying the cause of the abnormality.
Incidentally, in this example, diagnosis was performed in a frequency band of 5 kHz to 30 kHz in the high frequency region, 1 kHz to 10 kHz in the middle frequency region, and 5 Hz to 1 kHz in the low frequency region.

== About Diagnosis Method, Trend Monitoring, and Life Prediction Using Vibration Data According to this Embodiment ==
A diagnosis method, trend monitoring, and life prediction based on vibration data of the target facility according to the present embodiment will be described.
In the present specification, as the target equipment, a rotating machine, for example, a stirrer driven by a motor will be described as an example, but the target equipment is naturally not limited to this. Absent.
Here, an embodiment in which the present invention is applied to the detection of the occurrence of a bearing flaw, its progress, and life prediction in the diagnosis of a bearing flaw of a stirrer, misalignment (axial misalignment), and deterioration phenomenon of unbalanced rotation will be shown.

FIG. 3 shows the time course of the deterioration-specific dimensionless feature quantities Z1, Z5, and Z8 since the start of equipment diagnosis using vibration data.
A bearing flaw is a characteristic quantity Z1, a misalignment is a characteristic quantity Z5, and an unbalanced rotation is a characteristic quantity Z8 corresponding to each deterioration event. In FIG. 3, when the feature amount Z1 has passed 22.0 months, it exceeds the primary attention threshold value 8.0, and then reaches the second attention threshold value 20 after 24.7 months. ing. On the other hand, as shown in FIG. 4, during this period, the feature quantity Z5 exceeds the primary attention threshold after 24.0 months, and the feature quantity Z8 does not reach the primary attention threshold. That is, when the deterioration state of the bearing flaw is in the late stage of the attention area, it can be understood that the deterioration state of the misalignment is also in the attention area.

Deterioration of bearing damage has progressed in advance, FIG. 4 shows the deterioration specific feature amount Z1 of the bearing damage, and FIG. 5 shows the transition of the average vibration velocity Vrms. FIG. 6 shows future vibration monitoring class classification according to the combination of the increase / decrease sign of the feature amount Z1 and the increase / decrease sign of the vibration speed average value Vrms. Here, the case of dividing into the following four classes is shown.
AA: Extending the diagnosis interval at the most recently predicted time A: Follow-up as it is B: Follow-up with a shortened diagnosis cycle C: Examining the relationship between the feature quantity Z1 and the vibration velocity average value Vrms Predict arrival time

  FIG. 7 shows the classification of the monitoring class, the prediction of Z1 and the vibration according to the classification of FIG. 6 for each diagnostic period of the feature amount Z1 and the vibration speed average value Vrms shown in FIGS. 4 and 5 after 22.0 months. The change of the index n in the power law of the ratio of the speed average value is shown. The feature quantity Z1 is predicted only in the case of class C, and the exponent n of the power law is corrected by comparing with the measured value of the Z1.

FIG. 8 shows how the power law exponent n is corrected.
That is, until the secondary caution threshold is reached after the deterioration-specific dimensionless feature value Z1 of the bearing flaw exceeds the primary caution threshold (T1 = 22.0 months to T2 = 24.7 months in FIG. 3). During the period, the progress characteristic of the degradation event is extracted as the exponent n of the power law by predicting and measuring the feature quantity Z1 according to the class classification of FIG. 6 and the power law of FIG. 8 and actually measuring the vibration velocity average value Vrms. To do. FIG. 7 shows the change of the exponent n of the power law.

It is an example that exceeded the secondary caution threshold value of the bearing damage deterioration specific dimensionless feature quantity Z1 which is the deterioration event at 0.67, 0.17 and finally 0.34 starting from the initial value 0.8. .
Let Vi be the average vibration velocity at a certain time i, and let Z1 (i) be the degradation-specific dimensionless feature value.
The prediction coefficient C is corrected from the predicted value and measured value of Z1 using the following [Equation 3] and [Equation 4].




As the prediction coefficient C of Z1

指数n と予測係数 C との関係を

The relationship between the index n and the prediction coefficient C

Regarding the prediction of bearing damage deterioration-specific dimensionless feature quantity Z1, the basis for applying the power law of vibration speed average value Vrms is that the fatigue phenomenon of the metal at the rotating part is proportional to Vrms, cracks and wear at the rotating part There is a physical phenomenon that occurs at the square of Vrms, and the influence that the physical phenomenon has on the progress of the degradation event does not reflect all vibration energy in the degradation progression. The problem is to solve the problem by correcting the index n.
Then, when the feature amount Z1 reaches the secondary attention threshold, the time when the feature amount Z1 reaches the danger threshold is predicted.
However, when the vibration data itself fluctuates, such as when the operation load of the target facility is large, an error tends to increase in the estimation of the deterioration progress based on the power side of the vibration speed average value Vrms. Regarding the subject, a detailed description will be given separately regarding the remaining life estimation based on the time change of the current data of the driving motor and the remaining life estimation step in consideration of the timing of collecting the vibration data.

  By the way, the above-mentioned deterioration progression model of bearing flaws is basically applicable to deterioration events such as misalignment and unbalanced rotation. The reason is described below.

First, misalignment (axis misalignment) will be described. The factors behind the axis shift are:
(1) Rotation imbalance due to uneven wear on the drive side and driven side
(2) The external force and displacement applied from the connecting pipe and the basic part of the machine body. Therefore, the increase in vibration energy indirectly causes the deterioration degree of the misalignment phenomenon through the increase in the degree of wear and unbalance, and thus has a deterioration prediction model and analogy in the damage progress of the bearing.

  Next, imbalance will be described. This deterioration factor is a mechanical or mechanistic factor such as uneven wear or inclination of the rotating body or shaft, or a poor holding state at the bearing. Predictive model and analogy in case of injury progress. In addition, when the direct cause of this imbalance is a chemical or electrochemical factor such as corrosion, the direct influence from the increase in vibration energy is small, but the increase in vibration energy, wear, eccentricity, etc. Since it enters a vicious circle through an increase in the degree of corrosion, the corrosion phenomenon also becomes an indirect factor, which is consistent with the life prediction model of the present invention in which vibration energy is considered.

Then, when the feature amount Z1 reaches the secondary attention threshold, the time when the feature amount Z1 reaches the danger threshold is predicted.

If such a trend monitoring step and a life prediction step are executed, it is possible to make a prediction in consideration of characteristics during the progress of bearing damage, and the accuracy of the life prediction becomes practical.
The relationship between Z1, Z5, and Z8, which are specific dimensionless characteristic quantities for bearing flaws, misalignment, and unbalanced rotation, and the degree of physical progress in the actual deterioration state is shown in FIGS. 11 shows. These are findings obtained from the results of vibration tests for each deterioration event in a test apparatus using a simulator for a rotating machine. In these figures, the primary attention threshold, the secondary attention threshold, and the danger threshold in each deterioration event are shown.

Now, the situation and results of applying the diagnosis method, trend monitoring, and life prediction method of the present invention to a large agitator with an on-site drive motor capacity of 800 kW will be described.
As shown in FIG. 3, from the transition of each deterioration-specific dimensionless feature quantity Z1, Z5, Z8 in the bearing flaw, misalignment, unbalanced simple diagnosis, and trend monitoring, and the deterioration tendency monitoring result of the bearing flaw, The result of life prediction that the risk threshold is reached after T3 = 31.8 months from the start was obtained. This is because when the feature value Z1 related to the bearing flaw reaches the first caution threshold value 8.0, the time T1 = 22.0 months after the feature value Z1 reaches the second caution threshold value T2 = 24.7. In the period up to a month, the actual value of the feature amount Z1 and the vibration velocity average value Vrms shown in FIGS. 4 and 5 and the class classification of FIG. 6 and the prediction from the exponent change of the power law of FIGS. 7 and 8 are repeated. Is performed, and when the second attention threshold 20 of the feature amount Z1 corresponding to the bearing flaw is exceeded, the time when the risk threshold 28 of the Z1 is reached is predicted.

  After executing the above life prediction, it was verified that the accuracy of the life prediction was sufficient by careful operation of the target stirrer. In other words, after the predicted time T3 = 31.8 months, the agitator was stopped and disassembled and the target bearing portion was inspected, and the depth of the outer ring flaw of the bearing was about 600 μm. It was revealed that the deterioration specific dimensionless feature amount Z1 shown was almost identical to the danger threshold 28, and the lifetime prediction method was effective.

= Determining the optimal maintenance time based on vibration data of the target facility according to this embodiment =
First, a method for estimating the amount of energy loss caused by the progress of the deterioration event from vibration data will be described. FIG. 12 shows the misalignment amount and the loss rate of the motor power for the target pump.
FIG. 13 shows the unbalance amount and the loss ratio of the motor power.

  When the deterioration event is misalignment, it is obtained from vibration data based on the relationship between the deterioration-specific dimensionless feature amount Z5 shown in FIG. 10 and the misalignment amount (axis misalignment amount) between the agitator main shaft and the motor shaft in the field. Further, the misalignment amount is estimated from the value of the feature amount Z5, and the energy loss amount due to the deterioration can be estimated from FIG.

Similarly, in the case of unbalance, the unbalance amount is estimated from the deterioration-specific dimensionless feature amount Z8 obtained from the vibration data using FIG. 11, and the amount of energy loss due to the deterioration is estimated using FIG. It becomes possible.
The amount of energy loss in accordance with the progress of the bearing flaw is not considered because it is small compared to the deterioration events such as misalignment and imbalance described above, but the progress of the bearing flaw is less than that of other deterioration events. If it cannot be ignored, the energy loss ratio is obtained in advance by the same method as in the present invention.

  Next, the deterioration of the bearing damage progresses, and it becomes possible to predict the arrival time to the dangerous area by the trend monitoring step and the vibration life prediction step. As a result, an energy burden is generated. This energy burden can be quantitatively evaluated as the amount of carbon dioxide emissions required for manufacturing the bearing component.

Carbon dioxide emissions related to rolling bearing materials, production, and transportation are described in a report investigated and examined by the Japan Bearing Industry Association Global Environment Committee.
("Investigation and research on life cycle assessment (LCA) of rolling bearings")

As a result of applying the diagnosis method, trend monitoring, and life prediction method of the present invention to the above-described actual machine agitator with a motor capacity of 800 kW, as shown in FIG. T3 = 31.8 months.
Based on the results of the deterioration monitoring and vibration life prediction, misalignment, increase of power energy loss due to progress of deterioration of imbalance, energy burden due to replacement of new bearing parts, and damage caused by misalignment to the internal components of the target agitator The energy burden in the case of recovery, the increase in power energy loss due to the stirring efficiency decreasing by 1.5% per year from the start of operation, and the number of days when the stirrer was stopped for recovery of the deteriorated part of the stirrer. FIG. 14 shows the result of examining the elapsed months in order to determine the optimal maintenance time when considering the corresponding production loss.
From the results shown in FIG. 14, it can be seen that the total cost is minimized after 28 to 30 months from the start of diagnosis, and the replacement of the deteriorated bearing parts and the maintenance work for the alignment adjustment are executed during the period. It turned out to be optimal.

    In addition, the new replacement of the bearing parts described above is evaluated as the carbon dioxide emission amount related to the manufacture of the bearing, and the energy burden obtained by converting the emission amount into costs and the restoration of the internal parts of the agitator corresponding to misalignment are supported. The optimum maintenance time can also be determined by evaluating as the carbon dioxide emission amount and calculating the energy burden obtained by converting the emission amount into the cost as the maintenance cost every month.

  Here, the deteriorated bearing part is model NU306, and the amount of carbon dioxide emission for the production including the material of the part is 1, referring to “Investigation / Research of Life Cycle Assessment (LCA) of Rolling Bearing”. 190 kgCO2, carbon dioxide emissions related to the production of the target stirrer is estimated at 53,000 kgCO2, estimated from “LCA in production activities of large pumps”, and the ratio of the misalignment to the internal damage of the stirrer is about 5.5% As (2,920 kgCO2), the total amount of discharge of the bearing parts is 4,100 kgCO2.

  The carbon dioxide emission was set at 170 yen / kg CO2 as the standard unit price for environmental load, and the energy burden of new bearing parts was estimated to be about 200,000 yen, and the energy burden related to misalignment adjustment was estimated to be about 500,000 yen.

  In the above-described embodiment, the amount of energy burdened by the replacement of the bearing part with a new one and the alignment adjustment work for correcting misalignment (axial misalignment) is evaluated as the carbon dioxide emission generated by the maintenance, and the emission amount Was converted into the amount of maintenance cost because the production loss due to the suspension of the target agitator for the maintenance work was evaluated as the cost (amount). For the purpose of optimizing the maintenance time, carbon dioxide emissions It is also possible to search for the time when the amount of emissions is minimized by using the amount as a unified index.

== Based on the current data of the drive motor of the target equipment according to the present embodiment
Remaining life prediction method ===
FIG. 16 shows the overall configuration of the target equipment, such as a stirrer, a drive motor, an electric panel, and an internet connector.
The raw material is intermittently charged from the raw material inlet, stirred by a stirrer, and discharged as a product from the product outlet. In the agitator, the blades are rotated from the drive motor via the bearing. Rotational energy is supplied to the drive motor from the power panel at a voltage of, for example, 400V. A voltage signal from the acceleration sensor attached to the bearing unit, a current signal supplied from the electric panel to the drive motor, and an ON / OFF signal for starting and stopping the agitator of the target equipment are input to the Internet connector.

  A measurement example of the motor current value for driving the stirrer of the target equipment is shown in FIG. 17 with the operation time on the horizontal axis and the motor current value on the vertical axis. First, when the driving motor is started, the starting current rises greatly, and then temporarily decreases to a current value in a state where there is no operating load. Next, when raw material charging is started, a load due to product production is applied by the stirrer, and the motor current value rises, and it can be seen that the motor current changes corresponding to the intermittent raw material charging / stirring work. Then, after about 45 minutes from the start of operation, the raw material charging is finished, and at the stage where the product manufacture is completed, the motor current value gradually decreases to a no-load current value.

  Evaluation of repetitive fluctuation stress applied to the rotating part of the stirrer involved in metal fatigue, with respect to the time change of the current value of the drive motor from the raw material charging time to the completion of product manufacturing from the raw material charging time 18 shows the relationship between the motor current value and the applied stress, FIG. 19 shows the relationship between the number of repetitions of the variable stress and the stress width, and FIG. 21 shows the stress-repetition curve of the bearing material (normal SN curve). Called).

FIG. 18 shows that the rated current value 15 (A) of the stirrer driving motor, which is the target equipment, corresponds to the design value 8.0 (Kg / mm ² ) of the applied stress at the bearing portion during the rotation of the stirrer. From this, it can be obtained by a straight line connecting the origin, and by measuring the drive motor current value, the applied stress at the bearing portion can be estimated using FIG.

In FIG. 20, the time variation diagram of the motor current value of FIG. 17 described above is rotated 90 degrees so that time elapses from top to bottom.・ Up to 9 at the completion of product manufacture is the period for repeated stress evaluation. In this embodiment, the case where the raw material is input four times is shown.
Paying attention to the maximum and minimum values of the motor current value, the analysis of a certain stress width and the number of repetitions may be performed by a measurement method called a rain flow method. In this rain flow method, it is possible to obtain a half cycle stress width and the number of cycles, such as from 1 to 4, from 2 to 9,... As a result, the stress width in each cycle was as follows: cycle (a); 7.1 kg / mm ² , (b); 5.6 kg / mm ² , (c); 1.9 kg / mm ² , (d); .9Kg / mm ² ,
(E); 2.2 kg / mm ² , (f); 1.8 kg / mm ² .
Here, the magnitude of the applied stress corresponding to the maximum and minimum values of the motor current value in FIG. 20 can be obtained by conversion using FIG. 18 described above, and is shown in FIG.
The rainflow method is described in detail in [Non-Patent Document 1].
[Non-Patent Document 1]
Endo Tatsuo et al., "Fatigue of Materials Subjected to Fluctuating Stress-Estimation of Fatigue Life (1st Report)"
Proceedings of the Japan Society of Mechanical Engineers No.185, pp.41-48

Next, based on the stress cycles (A), (B), (C), (D), (E), and (F) obtained in FIG. 20, the bearing material stress-repetition curve shown in FIG. From the above, the number of repetitions N which becomes the fatigue limit in each cycle is obtained. As a result, cycle (A); N A = 1.02E + 05, (B); N B = 1.25E + 05, (C); N C = 3.08E + 05, (D); N D = 4.91E + 05, (e); N ho = 4.91E + 05,
(F); N f = 6.02E + 05.

With respect to the number of repetitions N which becomes the fatigue limit in each stress cycle obtained from FIG.
The remaining life of the bearing portion of the stirrer that is the target equipment by the operation in the present embodiment can be obtained from the life consumption rate D obtained by the following [Equation 5].

なお、当該寿命消耗率の定義については[非特許文献１]に記載されている。

The definition of the lifetime consumption rate is described in [Non-Patent Document 1].

In other words, if the operation time is about 70 minutes by one operation in this embodiment, and if this operation pattern is repeated, the life of the bearing due to metal fatigue is
70 (minutes) / 5.36E-05 = 1.31E + 06 (minutes) = 2.5 years (30 months)
It can be estimated that.

== Life prediction is made based on vibration data and current data of the target equipment, and the optimal maintenance time is determined.
Apparatus for determining ==
Next, the optimum maintenance time determination support device for the target facility for realizing the above-described simple diagnosis, precise diagnosis, deterioration tendency monitoring, life prediction, and optimum maintenance time determination method described above (hereinafter also referred to as the maintenance support device) An example will be described with reference to FIG. FIG. 22 is a conceptual diagram illustrating an example of a maintenance support device.

  In the present embodiment, the maintenance support device includes the maintenance support device main body 3, a vibration sensor 1, and a current sensor 2. The maintenance support device body 3 includes an A / D converter 100 that converts an analog electrical signal from the vibration sensor and current sensor 2 into a digital signal, the simple diagnosis, the precise diagnosis, the trend monitoring, and the life shown in FIG. Main computer 200 for performing prediction, energy loss evaluation, recovery energy burden evaluation, remaining life calculation, calculation and determination of optimum maintenance time determination, treatment, degradation energy loss database 300, recovery energy burden database 400, bearing material An SN curve database 500 and a result display 600 that displays the result of the main computer 200 are provided.

The main computer 200 has a computer program 200a for realizing the above-described life prediction and optimum maintenance time determination method, and the above-described computer program 200a is processed by a CPU provided in the main computer 200, so that the above-described computer program 200a is processed. Life prediction and optimum maintenance time determination methods are executed. The computer program 200a is composed of codes for executing the above-described life prediction and optimum maintenance time determination method. The main computer 200 may be composed of a plurality of devices instead of a single device. At this time, the computer program 200a may be provided separately for each of a plurality of devices.
The result display 600 has a function of giving various types of information to the operator of the maintenance support device 3 that executes quality determination, deterioration state monitoring, life prediction, and optimum maintenance time determination of the target equipment.

=== Other Embodiments ===
As described above, the simple diagnosis, precise diagnosis, deterioration tendency monitoring, life prediction, and optimum maintenance time determination method of the target facility according to the present invention have been described based on the above-described embodiments. The present invention is not intended to limit the present invention. The present invention can be changed and improved without departing from the gist thereof, and the present invention includes the equivalents thereof.

In the above embodiment of the present invention, in the attention area in the deterioration tendency monitoring.
Among all values of (Vi / Vi-1) ≧ 1.0 (degradation does not progress when <1.0)
(1) Maximum value: When the vibration energy to the deteriorated part shows a sharp progress
(2) Average value ・ ・ ・ ・ When there is a moderate change in progress and can be represented by averaging
(3) Minimum value .... It is also possible to adopt one of the cases where the influence on the deterioration progress of vibration energy is relatively small, and to perform life prediction when the secondary caution threshold is exceeded. This is one of the embodiments.

That is, in the above-described embodiment, the prediction is made based on the most recent increase or decrease in Z1 and V when the secondary attention threshold is exceeded. However, when the progress of the deterioration state in the attention region varies, the prediction is made only in the latest situation. As the accuracy decreases, the deterioration characteristics in the entire attention area exceeding the primary attention threshold and up to the secondary attention threshold are averaged, or the maximum and minimum values are used to represent the deterioration progress. Is another embodiment of the present invention.

1 is an overall flowchart of the present invention. It is the classification of the comprehensive judgment procedure and the monitoring treatment of the three judgment methods. It is the figure which showed the transition of the deterioration peculiar dimensionless feature-value corresponding to a bearing flaw, misalignment, and imbalance, and the lifetime prediction result. It is a transition of the deterioration specific feature amount corresponding to the bearing flaw. It is transition of the actual measurement value of the vibration speed average value corresponding to FIG. FIG. 5 is a class classification diagram of deterioration progress by a combination of a deterioration specific feature and a vibration speed average value. It is a change figure of the exponent of the power law in deterioration tendency monitoring. It is the transition between the predicted value and the actual measurement value according to the power law in the progress of deterioration of bearing damage. It is a figure which shows the relationship between the magnitude | size of a bearing flaw, and the said deterioration specific dimensionless feature-value. It is a figure which shows the relationship between the amount of misalignment and the said deterioration specific dimensionless feature-value. It is a figure which shows the relationship between an imbalance amount and the said degradation specific dimensionless feature-value. It is a figure which shows the relationship between the amount of misalignment, and the ratio of the said power loss. It is a figure which shows the relationship between the amount of imbalance and the ratio of the said electric power loss. It is a figure which shows the Example of the optimal maintenance time determination support of this invention. It is an evaluation figure of the total cost for the optimal maintenance time determination of this invention. It is a block diagram of the object installation of the Example of this invention. It is a figure which shows the change condition at the time of the driving | operation of the motor current value of the Example of this invention. It is a figure which shows the relationship between the applied stress and motor current of the Example of this invention. It is a figure which shows the relationship between the frequency | count of repetition of the fluctuating stress, and a stress width in the Example of this invention. It is the figure which evaluated the repetition of the stress from the electric current value change in this invention Example. FIG. 6 is a diagram in which the number of repetitions that becomes the fatigue limit is examined from the stress-repeat curve of the bearing material in the embodiment of the present invention. It is a figure which shows the assistance apparatus of lifetime prediction and the optimal maintenance time determination of an Example of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Vibration sensor 2 Current sensor 3 Optimal maintenance time determination support apparatus 100 A / D converter 200 Main computer 200a Computer program 300 Degraded energy loss database 400 Recovery energy burden database 500 Material SN curve database 600 Display
400 Restoration energy burden database 500 Material SN curve database 600 Display

Claims (6)

Based on the time variation of the electric motor of the electric power or current of the equipment in question, estimates the number of repetitions of change stress bearing portion, and the step 1 of determining the remaining life seeking lifetime wear rate, obtained from the vibration sensor attached to the bearing part The progress of the deterioration state is tracked by the power law of the time rate of change of the vibration speed to be estimated, and the arrival time to the dangerous area is estimated. Step 2 As a result of the vibration diagnosis in the step 2, Step 3 for determining the remaining life from Step 1 and Step 2, and determining the life of the target equipment from the two remaining life periods of Step 1 and Step 2 in Step 3 above. Method.   The remaining life determined in step 1 according to claim 1 and the dangerous area arrival time determined in step 2 are compared, and any one of the shorter times is determined as the remaining life.   In step 3 according to claim 1, if there is a difference exceeding a certain threshold between the remaining life period in step 1 and the dangerous area arrival time in step 2, oil analysis other than vibration / electric power or ultrasonic waves A method for determining the life of a target facility, wherein a remaining life is determined by performing a precise diagnosis using a signal or the like.   The time signal from the vibration sensor according to claim 1, the time signal from the power or current sensor, and the power ON-OFF signal to the motor of the target equipment are used as input signals, and the vibration sensor is triggered by the ON-OFF signal. A method for determining the lifetime of a target facility, characterized by determining a time signal capturing time.   The computer program for implement | achieving the diagnostic method of the object installation of Claim 1. As vibration data of the target equipment, at least one of vibration displacement, vibration speed, vibration acceleration, vibration acceleration skewness, vibration acceleration kurtosis, and vibration acceleration crest factor, and vibration acceleration waveform A sensor for acquiring data, a degradation energy loss database, a recovery energy burden database, a SN curve database of bearing materials, and a computer having the computer program according to claim 5. A device for diagnosing the target equipment.

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