JPH0281299A - Trouble forecasting device - Google Patents

Abstract

PURPOSE:To easily forecast trouble before the occurrence of trouble in a product by only inputting data of periodical inspection, etc., by generating a hazard Weibull chart in accordance with a trouble discrimination level to a calculate an inclination (m) and outputting the inclination (m) for discrimination of the latent trouble state of the product. CONSTITUTION:Periodical inspection data is inputted by an input device 2, and an operation processing device 1 calculates the error between input values and reference values and stores error calculation results or the like in the memory of a microcomputer, and an error decision level lower than the changeable level for decision of general trouble is inputted by a maintenance man. The processing device performs the trouble deciding operation and regards products exceeding the decision level as faulty products and automatically generates a hazard Weibull chart 4 and calculates the inclination (m). The value of the fixed-form parameter (m) is outputted from the operation processing device 1 to an output device 3 to output numbers and trouble contents of measuring meters which decide trouble. Since the inclination (m) larger than one indicates the existence of latent trouble before trouble due to wear, preventive maintenance is performed by the value of the inclination (m).

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention relates to a failure prediction device used for preventive maintenance of various products, and is capable of predicting failure without requiring advanced specialized knowledge regarding destructive inspection or deterioration of products/parts. The present invention relates to a failure prediction device suitable for implementing preventive maintenance before a failure occurs based on periodic inspection data and the like.

[Conventional technology]

Conventional periodic inspection data processing devices are only used as office equipment that performs error calculations based on periodic inspection data input from an input unit and outputs the results on standardized recording forms, and are used only for preventive maintenance. This was a technology that required highly specialized knowledge, such as determining the degree of deterioration by the judgment of experts or by destructive inspection of sampling instruments. Furthermore, the conventional hazard Weibull chart method is a maintenance method that statistically processes failed products and parts, such as cumulative failures, to estimate the condition of remaining healthy products and parts.

In addition, as a conventional deterioration estimation method and deterioration diagnosis device for this type of products and parts, for example, Japanese Patent Application Laid-Open No. 58-61474 uses a small number of samples by assuming that the distribution state of failures can be represented by a normal distribution. We are proposing a method that makes it possible to estimate a small failure probability and to mathematically express the secular change of a normal distribution, thereby making it possible to predict the lifespan for a longer period than the period in which the lifespan test was conducted. Furthermore, Japanese Patent Application Laid-Open No. 134568/1983 proposes an apparatus that compares each deterioration state with samples that have been evaluated through experiments in advance, rather than using a statistical method. However, these methods and devices all judge failure based on whether or not the characteristic value is below the reference failure limit value, and there are no methods for predicting failure of products and parts that have not yet reached the failure limit. It has not been.

[Problem to be solved by the invention]

In the above-mentioned conventional technology, the degree of deterioration is determined by the judgment of an expert or by destructive inspection of sampling instruments, etc.
Or create a hazard Weibull chart based on failure time and number of occurrences after instrument failure and slope (shape parameter)
They are classified into initial failures, random failures, and wear-out failures based on m (see Figure 6), and are used for preventive maintenance (see Figure 6), all of which have problems that require a high degree of specialized knowledge, a great deal of labor, and time.

Another problem is that the failure determination method using the Hazard-Weibull chart cannot be used unless the product fails.

SUMMARY OF THE INVENTION An object of the present invention is to provide a failure prediction device that can easily predict failures before product failures by simply inputting data such as periodic inspection data and can be used for preventive maintenance.

[Means to solve the problem]

The above purpose is to provide a means for inputting periodic inspection data of products such as meters, and to sequentially compare error data obtained by comparing the input data value and true value with a variable failure judgment level, and to exceed this failure judgment level. It is equipped with an arithmetic processing unit that regards a product as a failure including a potential failure, creates a hazard Weibull chart, calculates and outputs the slope m, and an output device that outputs the arithmetic processing result, and is equipped with an output device that outputs the result of the arithmetic processing. This is achieved by a failure prediction device configured to determine potential failure states such as wear-out failures and perform preventive maintenance.

[Effect]

The failure prediction device inputs periodic inspection data, etc., calculates an error by comparing this input value with a true value for calibration, stores the calculation result in a storage unit, and then inputs a certain failure judgment level. By comparing this failure judgment level and the error of the above calculation result, a product that exceeds the judgment level is regarded as a defective product, and from this a hazard Weibull chart is automatically created and the slope m is calculated. , here, the failure determination level can be changed arbitrarily by maintenance personnel, and by inputting a failure determination level lower than the level that is generally determined to be a failure, the slope m of the hazard Weibull chart obtained at this time is less than 1. If the value is large, it includes potential failures before entering the wear-out failure period, and by predicting that the failure rate will increase in the future, it can be seen that preventive maintenance is necessary, so it is possible to detect potential failures before the product completely fails. This makes it possible to carry out preventive maintenance in a planned manner.

〔Example〕

An embodiment of the present invention will be described below with reference to FIGS. 1 to 6.

FIG. 1 is a basic arithmetic processing flowchart showing an embodiment of the failure prediction device according to the present invention. In FIG. 1, first, in process lOO, instrument number 2 pre-calibration output 2 calibration values, etc. of periodic inspection data are inputted through input device (input unit) 2. In process 101, the arithmetic processing unit 101 calculates the error between the input value (output before calibration) and the reference value 10, and in process 102, the instrument number, inspection port, operation start date, error calculation result, etc. are stored in the memory of the microcomputer. Store. Then processing 10
In step 104, an error determination level (failure determination level) lower than the level for determining a general failure, which can be changed, is inputted by the variable human power of the maintenance staff No. 3. In process 105, the arithmetic processing unit 1 performs a failure determination calculation under the condition that the error calculation result exceeds the error determination level, and in process 106, it plots a graph from the error value and operating time t of the instrument determined to be malfunctioning, and then plots a graph based on the error value and operating time t of the instrument determined to be malfunctioning. A Hazard-Weibull chart creation calculation is performed to draw the closest straight line, and in step 107, a shape parameter m, which is the slope of the straight line, is determined. Based on the above calculation results, the processing unit 1 outputs the periodic inspection prohibition output from the output bag [3 in process 108, outputs the value of the shape parameter m in process 109, and outputs the above failure (including potential failure) in process 110. ) and the failure details are output. Further, the hazard Weibull chart created in process 106 may be output. With these outputs, potential failure states such as initial failure, random failure, wear-out failure, etc. can be determined from the value of the slope m, and preventive maintenance of instruments can be performed.

FIG. 2 is a structural diagram of a data processing device showing an embodiment of the failure prediction device according to the present invention. In FIG. 2, reference numeral 1 denotes an arithmetic processing unit equipped with a failure prediction calculation processing function, which includes a microcomputer, a storage section (memory), and the like. 2
3 is an input device for inputting periodic inspection data, etc., and 3 is an output device for outputting a hazard Weibull chart, etc. of the arithmetic processing results, and includes a CRT display device, a printer, etc. With this configuration, periodic inspection data of instruments, etc. is inputted to the arithmetic processing unit 1 from a data input device 2 such as a keyboard, and the microcomputer compares and calculates this input value with a standard value that is used as a reference for periodic inspections. After calculating the error, the error calculation results and the like are stored in a storage unit (memory). An example of this stylized output is shown below.

FIG. 3 is a standard output data pattern diagram of the periodic inspection data shown in FIG. In FIG. 3, numeral 5 is an example of a standardized periodic inspection table output. The periodic inspection table 5 records the inspection date, instrument number, product number, product use start date, etc. Also, the reference input (% + mA) for the instrument to be calibrated, e reference output (
mV), constant output before calibration (mV), error between output before calibration and reference output (%)9 Calibration value (mV) which is the measurement output of the calibration machine
, the error (%) between the calibration value and the reference output, and other values are recorded.

Furthermore, the values of the errors to be noted (before calibration) are displayed in a graph, and all values are within ±0.5% of the product quality judgment error, so this judgment error is ±0.5%9. recorded. Error in this periodic inspection data (before calibration)
The graph below shows the maximum values of .

FIG. 4 is a diagram showing the secular change in the instrument error distribution of the periodic inspection data shown in FIG. In FIG. 4, the error percentage (before calibration) in FIG. 3 is collected for all instruments (products) subject to periodic inspection, and is plotted and displayed in a graph against the period of use (operating time) in years.

This error% is calculated by plotting the maximum absolute value maxl e l of the error (before calibration) value e in Figure 3.
In the example shown in the figure, 0.48 is the plot value. Here, the failure judgment level (pass/fail judgment error) of the product in the periodic inspection data can be changed arbitrarily by maintenance personnel, and for example, after the change The value of failure determination level 0.4% is input from the input device 2. The microcomputer in the arithmetic processing unit 1 detects errors in periodic inspection data stored in the storage unit (before calibration).
Calculation result and failure judgment level after change (pass/fail judgment error) ±
A comparison operation is made with 0.4%, and a product with an error exceeding this determination level is regarded as a defective product (including a potentially defective product), and its instrument number etc. are restored. Therefore, for example, the data in periodic inspection table 5 shown in FIG.
Next, the arithmetic processing bag M1 creates a hazard Weibull chart for the product deemed to be failed at the changed failure determination level.

FIG. 5 is an output pattern of the hazard Weibull chart of the periodic inspection data shown in FIG. In Figure 5, 4 is an example of Hazard Weibull chart output.

The cumulative number of failures H(t) with respect to usage time t when the failure determination level is XI+12% is displayed. The microcomputer of the arithmetic processing unit 1 described above has a failure judgment level of Xl.
+
Hazard Weibull Chart 4
After automatically plotting the cumulative number of failures H(t) corresponding to the usage time t of and output to the output device 3. Next, the failure period of the product can be determined based on the value of the slope (shape parameter) m of this straight line.

FIG. 6 is a typical equipment failure rate curve (life curve) based on the periodic inspection data shown in FIG. 2. In Figure 6, the failure rate λ(1) of a well-known typical equipment failure rate curve (life curve) with respect to operating time t is displayed.From this failure rate curve, the hazard Weibull chart 4 in Figure 5 is shown. If the slope of the straight line (shape parameter) m is larger than 1 and m > 1, these products can be considered to be about to enter the wear-out failure period, so the next periodic inspection of the restored products will be carried out. Preventive maintenance such as occasional replacement can be planned. Items of periodic inspection data used for this preventive maintenance include zero adjustment amount, span adjustment amount, linearity,
It may be based on input/output response characteristics, etc. Also, the slope (shape parameter) m = l, m (in the case of l,
It is also useful for short-term preventive maintenance by being able to determine the random failure period and early failure period. The period in which the failure rate is equal to or less than the prescribed failure rate corresponding to the random failure period (m=1) of this failure rate curve (life curve) is the useful life of the product.

〔Effect of the invention〕

According to the present invention, it is possible to easily predict product failures before they occur by using periodic inspection data of instruments without requiring advanced specialized knowledge, making it possible to formulate a preventive maintenance plan. When applied to measurement and control equipment, it has a great effect on improving the reliability not only of the measurement and control equipment, but also of plants, etc.

[Brief explanation of the drawing]

FIG. 1 is a basic arithmetic processing flowchart showing an embodiment of the failure prediction device according to the present invention, and FIG. 2 is a side view of the configuration of a data processing device showing an embodiment of the failure prediction device according to the present invention.
Figure 3 is the standard output side view of the periodic inspection data in Figure 2,
The figure is a side view of the secular variation of the instrument error distribution of the periodic inspection data in Figure 2, Figure 5 is the output side of the hazard Weibull chart of the periodic inspection data in Figure 2, and Figure 6 is the periodic inspection data in Figure 2. FIG. 3 is a typical equipment failure rate curve diagram of the data. 1... Arithmetic processing device equipped with failure prediction calculation processing function,
2... Input device for inputting periodic inspection data, etc., 3...
- Output device that outputs a hazard Weibull chart etc. of the calculation processing result, 4... Example of outputting a hazard Weibull chart, 5... Example of outputting a standardized periodic inspection sheet.

Claims (1)

[Claims] 1. Input means for inputting periodic inspection data etc. obtained during periodic inspection of products such as meters, and periodic inspection data to be input;
A means for inputting a variable failure judgment level for accumulated data such as error data, and a means for performing a comparison operation between the accumulated data and the variable failure judgment level to create a hazard Weibull chart according to the failure judgment level. A failure prediction system consisting of an arithmetic processing unit that calculates the slope m, and an output device that outputs the slope m of the hazard Weibull chart resulting from the calculation for use in determining potential failure states such as initial failures, random failures, and wear-out failures of the product. Device.