Criteria to Analyse Performance (Step 3)

The criteria are used to measure the performance of the different failure detection approaches. The selection is very important, since the result will be different, if not all relevant criteria are included.

As most of the methods are still in development, a qualitative evaluation is applied. The criteria are as follows:

• Automatic Failure detection included?

• Accuracy/effectiveness of failure detection

• Automatic Failure identification included?

• Accuracy/effectiveness of failure identification

• Costs (operational and hardware)

3. Overview of Failure Detection Methods

The methods for failure detection will be described in the next sections. Table 3.1 lists some characteristics of the different methods, like what time scale and for what type of systems they are applied to.

Table 3.1 Overview of several Characteristics of Methods

Characteristics

MM

OPT

FUKS

SPM

IOC

ANN

GRS

KU

Time scale of data logging1

Var

15 min

<1 min

1 sec

min

Hour?

var

1 min

Time scale of analysis

Var

?

sec

day

hour

Mon or yr

10 min and day

Simulation?

No

No

No

No

Yes

Yes

Yes

Yes

Scale of the system (tested) (collector area in m2)

Var

30-250

5

7-16

2-455

402

Very

large

88-400

Type of system

Var

DHW

Combi

DHW

DHW

(simpl)

DHW

DHW

Stage of development[11]

++

++

++

++

++

+-

Level of automation[12]

++

++

++

++

+-

++

1 Time scale: var = variable, sec = second, min = minute, hour, day = day, mon = month, yr = year

2 TRNSYS simulation

key figures were studied. These are compared to the values determined in the planning phase, which are based on the irradiation and temperature profile of a typical reference year [2].

The optimisation phase was very effective regarding failure detection, however, it is time-consuming and therefore costly. The routine supervision phase is not that time-consuming, but does not deliver a quick feedback if the system is working properly and it does not locate failures.