JPH04113247A - Diagnosing apparatus of equipment - Google Patents

Abstract

PURPOSE:To definitely detect a point of transition to a random failure period and a point of transition to a wear-out failure period by evaluating the state of equipment by calculating an extension rate m(t1) of MTBF every time when a prescribed period ta passes after passage of a prescribed period to from completion. CONSTITUTION:A first MTBF calculating means which calculates MTBF (to) of equipment as to a prescribed period to from completion, a second calculating means of mean time between failures which calculates MTBF (ti) of the equipment as to a period ti obtained by adding a prescribed period taXi (i=1, 2,..., n) to the aforesaid period to, and an extension rate calculating means which calculates an extension rate m(ti) by subtracting MTBF (to) from MTBF (ti) and by dividing the remainder by MTBF (to), are provided. The state of the equipment is evaluated on the basis of a stage according to a standard deviation set beforehand on the basis of a regular distribution curve, to which the extension rate m(ti) calculated by the extension rate calculating means belongs and the result of the evaluation is displayed.

Description

DETAILED DESCRIPTION OF THE INVENTION [Industrial Field of Application] The present invention relates to an equipment diagnostic device that provides advice regarding the status of equipment.

[Conventional technology]

Various systems are installed in high-rise buildings and the like. For example, such systems include air conditioning systems, elevator systems, lighting systems, disaster prevention/crime prevention systems, etc., and building equipment is constructed by combining these systems.

When a failure or abnormality occurs in building equipment, repairs or replacements are carried out as appropriate, but some of the functions of the building equipment will stop until the repairs or replacements are completed.

For this reason, a maintenance plan is usually created and building equipment is inspected periodically based on this maintenance plan. This maintenance plan is created using the cycles estimated from past experience and the cycles recommended by the manufacturer.
Shorten the inspection cycle for a while after the building is completed (after the building equipment starts operating).

That is, in building equipment, the relationship between its failure rate and operating time is represented by a Hastuff curve as shown in FIG. According to this bathtub curve, it can be seen that the failure rate is high after the start of operation of building equipment, the failure rate decreases as the equipment continues to operate, it eventually reaches a stable period, and the failure rate increases as the service life approaches. In other words, the mean time between failures MT of building equipment
BF (Mean Time Betimeen Fai)
Lures) is small at the beginning of operation, increases as the operation continues, reaches a stable period, and decreases as the life approaches. For this reason, the inspection cycle is shortened for DFR during the initial failure period (unstable state) from the start of operation to P3 hours, and longer for CFR during the accidental failure period (stable state) from P+ time to P2 hours. Wear failure period (unstable state) IFR after 22 hours
Inspection intervals will be shortened.

[Problem to be solved by the invention]

However, in the past, there was no effective criterion for determining the transition point P from the initial failure period DFR to the random failure period CFR, or the transition point P2 from the random failure period CFR to the wear-out failure period IFR. There is no valid standard for evaluating the condition, and it is ambiguous.

For this reason, it is necessary to estimate the initial failure period DFR longer,
If the random failure period CFR is estimated too short, operation costs such as periodic inspection costs and personnel costs for securing maintenance personnel will increase, resulting in unnecessary expenditure.

There is an empirical law proposed by Duane in 1962 as a confidence growth model, and MIL-S
It is adopted in TD-1635.

The basic theory of this reliability growth model is that "as corrective measures are implemented during development testing, the MTBF of the equipment increases in proportion to the square root of the cumulative operating time." This is based on the fact that the relationship between MTBF and operating time is linear when plotted on a log-log graph. Expressing this in a formula, j2ogθ,=ml o gT+l o
gK' ・ ・ if) However, cumulative MTBF after θTNT time, m: growth rate of reliability, T: total test time (or cumulative operating time), ni': constant determined by environment.

When the logarithm is restored, θA='T''...(2) Or, λT=F/T=KT-'' However, λA: Cumulative failure rate after 1 hour, total number of failures in FAT time , ni=1/ni' The above equation (2) is called the equation of Duane's reliability growth model.

However, the credibility growth model proposed by Duane is primarily aimed at improving the design of products and cannot be applied to evaluation criteria for the condition of building equipment. That is,
The factors that change the credibility of building equipment differ from the reliability development/growth test of equipment in the following points.

■The proportion of failures in building equipment due to design defects is extremely low.

■Failures of building equipment are counted after actual operation begins, and failures are likely to occur in the early stages due to inexperience in handling or mistakes. As these failure-prone items are gradually corrected, they tend to decrease, resulting in a stable and low failure rate.

■When it comes to failures in building equipment, it is difficult to conduct repeated tests such as creating reliability growth models, and even if design improvements are made, it is unlikely to have much of an effect.

In other words, a concrete survey of building equipment failures shows that human errors such as mishandling, instructions, and communication errors are more common than those related to equipment design. Focusing on this point,
From the perspective of improvement through learning, it is hoped that simpler and more practical judgment criteria will emerge rather than modeling that requires repeated testing. That is, the reliability growth model proposed by Duane does not take human error into consideration, and is not an effective criterion for evaluating the condition of building equipment.

[Means to solve the problem]

The present invention was proposed in order to solve such problems, and it is based on the MTBF (t
0); and a first MTBF calculation means for calculating the period t.
0 for a predetermined period 1. xi (i=1゜2,...,n)
Period 1. MTBF (t
i') by subtracting MTBF(t0) from MTBF(t
elongation rate calculating means for calculating an elongation rate m(t,) by dividing by
The condition of the equipment is evaluated according to which stage (t=) belongs to a standard deviation set in advance based on a normal distribution curve, and the evaluation results are displayed.

[Effect]

Therefore, according to this invention, the predetermined period t after completion of construction
After the elapse of 0, it is possible to evaluate the state of the equipment by calculating the elongation rate m(ti) of the MTBF every time a predetermined period t elapses.

〔Example〕

Hereinafter, the equipment diagnosis device according to the present invention will be explained in detail. FIG. 2 is a diagram showing a building remote monitoring and control network including one embodiment of this equipment diagnosis device.

This remote monitoring and control network monitors the environment and hygiene of each building.

It monitors and controls various systems such as disaster prevention, and includes a central control device 1 that comprehensively manages these systems 10, and a plurality of systems 1 installed in the building.
0 is connected to control each of the building equipment 7-1 to 7n. Here, the distributed control device 2-1 is connected to control the building equipment 7-1, and the building equipment 7-1 is composed of 10 such as an air conditioning system, an elevator system, a lighting system, and a disaster prevention/crime prevention system. There is.

The central control device 1 includes a keyboard 32 for operation, a CRT 4 for screen display, and a printer 5 for printing out the displayed contents, and is located in the boss center 12. This central control device 1 processes and stores management data, manages and distributes shared data, and also processes and distributes shared data.
Controls control and system processing spanning 1 to 2-n. It also has a function as an equipment diagnosis device according to the present invention.

On the other hand, the distributed control devices 2-1 to 2-n are the building equipment 7-1 to
Performs alarm detection every 7-n, power failure/recovery control, various energy saving controls, etc. In addition, tJJ is added to the distributed control devices 2-1 to 2-2n.
The system 10 subjected to JE is provided with a failure detector 11 for each predetermined system device that constitutes the system. That is, the failure data for each system device detected by the failure detector 11 is transmitted to the distributed control devices 2-1 to 2-n.
The signal is sent to the central control unit 1 via a transmission line 6.

For system equipment that is not equipped with a failure detector 11, the failure data for each corresponding system equipment is
Manually enter the central control unit 1 via the keyboard 3.
Donate to.

FIG. 1 is a flowchart for explaining the equipment diagnosis operation by the central control device 1. As shown in FIG.

First, display the related menu on the CRT4 (Step 1
01) Looking at this display menu, if there is a building to be completed, the operator selects "Completed Building Information Registration".Step 102) (Step 103) and register it in the building management file (Step 104).

If you look at the display menu in step 101 and select "Input failure data" (step 105), you can input the failure data for each system device manually (step 106), and the failure data can be converted into failure data. It is registered in the file (step 107). Note that the failure data for each system device detected by the failure detector 11 is automatically registered in the failure data file.

When the user looks at the display menu at step 101 and selects "MTBF calculation" (step 108), the corresponding building name is displayed (step 109). Look at this displayed building name,
The operator may select the building for which the MTBF is to be calculated (step 110). Now, assuming that the building selected in step 110 has been completed for three months, the failure data up to that point is extracted with three months after completion as a predetermined period t0 (step 111), and the failure data of the building equipment in that building is extracted. MTB F (to) is calculated (step 1
12), registered in the building management file (Step 1)
13). If the building selected in step 110 is 6 months old after completion, a predetermined period t,
(3 months) xi (i=1) plus period t, the failure data up to that point is extracted (step 111
), the MTBF of the building equipment in that building (tl
') is calculated (step 112) and registered in the building management file (step 113). That is, MTBF (to
), and a predetermined period t, (3 months) Xi(
i=1.2. ..., n) period 1. (ti
=t, +t, xi) for MTBF (t.

) will be registered in the building management file.

When viewing the display menu in step 101 and selecting the [Initial failure evaluation] menu (step 114), the MTBF (t0) and M
TBF (ti) is displayed as a line graph (step 115). Then, for each building equipment 7-1 to 7-n,
That MTBF (to) and the latest MTBF (ti
), calculate the following formula to calculate the elongation rate m of MTBF.
(tH) is calculated (step 116).

Then, the building equipment 7 calculated in this step 116
Building equipment 7-1 to 7-n depends on where the extension rate m (ti) of MTBF for each of -1 to 7-n belongs to the standard deviation stage preset based on the normal distribution curve in education statistics. The status of n is evaluated and the evaluation result is displayed on the CRTd (step 117). The table below (
Table 1) shows the relationship between the deviation obtained from the standard normal distribution curve and the frequency ratio, and this distribution is calculated using the standard deviation as the unit and 1 σ for each step, similar to the 5-level grading Table 1 method in education. When grouped into five stages, the distribution will be as shown in Figure 3. When the distribution shown in Figure 3 is applied to the stabilization standards for building equipment, the results are as shown in Table 2. As can be seen from this table, the elongation rate m (tl) is m≦-0,43
32, “unstable state J, 0.4332< m≦
-0,1915 means "Slightly unstable DJ, -0,
If 1915<m<0.1915, [no change J,
If 0.1915≦m<0.4332, it is evaluated as a “slightly stable state,” and if 0.4332≦m, it is evaluated as a “stable state.”
The change from "no change" to "slightly stable state" to "stable state" shows the transition point from initial failure period DFR to random failure period CFR, and also from "stable state" to "slightly stable state". Due to the change from "no change" to "slightly unstable state" and "unstable state", the random failure period CF
The transition point from R to wear failure period IFR can be seen.

When the stabilization criteria shown in Table 2 was applied to an actual remotely monitored building, effective results were obtained as a criterion for determining early failure in building group management. Regarding this result, the −
An example is shown in Table 3, and each position in Table 3 is shown in a line graph for each building in Table 3 of Figure 4. In the line graph in Figure 4, I, n, III
When the state of the group shifts to the TV and V groups, and the state of the V group becomes continuous, this is determined to be a transition from the initial failure period DFR to the random failure period CFR. Although I have omitted the results, the status of group V is ■.

If we move on to group II and group I through group ①, this is judged to be the transition from random failure period CFR to wear-out failure period IFR.

As described above, according to this embodiment, the building equipment 7-1 to 7-n
For each case, the transition point from the initial failure period DFR to the random failure period CFR, and the transition point from the random failure period CFR to the wear-out failure period IFR.
Since it is possible to clearly know the transition point, it is possible to use it for planning, reviewing, and accurately applying the monitoring and inspection system, reducing periodic inspection costs and personnel costs for securing maintenance personnel. Operating costs will not increase, and you will no longer be forced to spend wastefully. It is also possible to evaluate equipment design and operation.

In this embodiment, the period t0 and the period t1 are set as three months, but they are not fixed to three months, and may be set in units of one day, one week, one month, etc.

Furthermore, in this embodiment, the explanation has been made assuming that advice regarding the status of the building equipment 7-1 to 7n is received, but system 1o is regarded as equipment and advice regarding the status of this equipment is received. Alternatively, each system device may be regarded as equipment, and advice regarding the status of this equipment may be received. In other words, the concept of "equipment" as used in the present invention is extremely wide.

Further, although this embodiment has been described as performing remote monitoring and control of each building, it is also possible to apply the present invention in a similar manner to the case where each building is independently monitored and controlled.

〔Effect of the invention〕

As is clear from the above explanation, according to the equipment diagnosis apparatus according to the present invention, after the predetermined period t0 has elapsed since the completion of construction, the predetermined period t.

It is possible to evaluate the condition of the equipment by calculating the elongation rate m (tl) of the MTBF every time the period elapses, and the displayed evaluation results can be used to determine the transition point from the initial failure period to the random failure period. Since it is possible to clearly know the transition point from the random failure period to the wear-out failure period, it is possible to use it for planning, reviewing, and accurately applying the monitoring and inspection system, and it is possible to save periodic inspection costs and maintenance personnel. Operational costs such as personnel costs will not increase, and you will no longer be forced to make unnecessary expenditures. It also becomes possible to evaluate equipment design and operation.

[Brief explanation of drawings]

FIG. 1 is a flowchart for explaining the equipment diagnosis operation by the central control device shown in FIG. 2, and FIG. 2 is a diagram showing a building remote monitoring and control network including an embodiment of the equipment diagnosis device according to the present invention. Figure 3 is a diagram showing stages according to preset standard deviations based on a normal distribution curve, Figure 4 is a diagram showing each position in Table 3 for each building as a line graph, and Figure 5 is a diagram showing building equipment. FIG. 3 is a diagram showing a bathtub curve representing the relationship between failure rate and operating time. 1... Central control device, 2-1 to 2-n... Distributed control device, 3... Keyboard, 4... CRT, 7-1 to
7-n... Building tt equipment, 10... System, 11.
...fault detector. Figure Figure Figure B2

Claims (1)

[Claims] The MTBF of the equipment (
t_0); and a first MTBF calculation means for calculating the period t_0 for a predetermined period t_a×i(i=1, 2, .
..., n) for the period t_i, M of the equipment
a second mean time-to-failure calculation means for calculating TBF (t_i); subtracting the MTBF (t_0) from the MTBF (t_i) and dividing by the MTBF (t_0) to obtain an elongation rate m;
(t_i), and the elongation rate m(t_i) calculated by the elongation rate calculation means, depending on which stage of the standard deviation preset based on the normal distribution curve belongs to the said equipment. An equipment diagnosis device comprising: an evaluation means for evaluating the state of the equipment; and a display means for displaying the evaluation results of the evaluation means.