Self-test is a distinctive feature of modern instruments. In fact, self-diagnosis is a very broad concept, modern electronic devices are inseparable from self-test, self-diagnosis system. Take the ASUS V6600Deluxe display card as an example. A small graphics card includes fan speed monitoring, AGP voltage monitoring, SmartCooling smart monitoring, overheat protection and dynamic overclocking. This is a hard and soft combination technology called SmartDoctor. The self-diagnosis referred to in modern instrumentation not only indicates that the instrument is broken, but responds, but it is detected and repaired when the instrument has potential failure or accuracy and characteristics are degraded. Instrumentation self-test, self-diagnosis systems are generally independent high-level hardware and software functional blocks. This microprocessor-based processing unit is logically different from data conversion, sampling and processing systems. The transmission and conversion of information is completed quickly and concisely within the system. Currently, the incentive-response "loopback" technique is still commonly used. In order to meet the high requirements of the self-diagnosis process, firstly, the key points in the instrument circuit should be found out, and the electrical parameters of various excited states and normal measurement states should be stored, and the historical records should also be stored; secondly, each closed loop can be used at any time. Turn on the “loopback†check, and the clock should be automatically adjusted at any time to complete different jobs during the self-diagnosis process.
1 is the process measurement instrument self-diagnosis system.
Sensitive or changing components of both electrical and process measuring instruments can age. For example, NTC thermistors, which are commonly used for temperature sensing, often need to be calibrated with reference variables. The following three methods are used for continuous or intermittent self-test and self-calibration of electrical measuring instruments.
a. Computational analysis Self-test The basic principle of this method is to measure a number of auxiliary variables related to it while detecting the main measured parameters, and then analyze and correct according to certain models and algorithms to test and correct the measurement process. And the result. Therefore, such meters generally output a status signal (additional output) in addition to the output measurement result (function output).
This method is also known as "redundant analysis self-test."
b. Superimposed signal self-test ((b)). While measuring the signal feed, continuously, intermittently or periodically send one or a group of signals, which may be high frequency or pulse signals.
It is superimposed on the measured signal and processed in the processing unit. After the specific model and algorithm are completed, the measurement data and status signal are output.
c. Automatic periodic self-test. As can be seen in 2(c), an automatic switch periodically inputs the measurement signal and some known variables into the system, processes and analyzes and outputs the measurement data and status signals. This method is effective, and its only drawback is that the measurement signal is discretized, which may cause distortion and error.
The intelligent measuring instrument needs to periodically and automatically test the hard and software of the instrument itself. There are two types of power-on self-test and timed self-test. The hardware self-test objects are ROM, RAM, and bus.
2 self-diagnosis of the meter input stage
Each instrument's self-test and self-diagnosis tasks require a series of operations within the instrument—metering verification and functional inspection. These operations are completely different from the normal measurement work of the instrument. They can be taken offline or online. . Each measuring instrument has an input stage, and 3 is a self-diagnostic module commonly used in the input stage of electrical measuring instruments.
This input stage self-diagnosis module has two detection points (thick black line), through which the operation status can be basically determined and relevant diagnosis can be performed. First, through the parallel SAR input port, input a series of pre-designed digital quantities for detection and diagnosis, and monitor the output value of the converter at any time. Functional status is accomplished by a comparator and an additional D/A converter. The main D/A converter allows the comparator to output the relevant pulse signal. In a coordinated operation of two D/A converters, the results of the static test can be obtained from the output of the comparator. The programmable voltage reference source is used to correct this successive approximation A/D converter. The dynamic detection of the entire A/D converter is an excitation source with an additional D/A converter, which is fed into the preset by the parallel port of the SAR.
The self-calibration procedure is divided into two steps. The first step is the static calibration period, which mainly corrects the measurement channel. Of course, the check value is also stored and compared with the history. The second step is to detect the various functions of the instrument under dynamic conditions, and then use the triangular wave (for A/D converter), etc. to perform the "return". Programmable high-frequency triangular waves can be used to detect high-speed sampling measurement channels whose conversion frequency, slew rate, time and amplitude reflect their functional status. Superimposing some micro-step pulses at the standard voltage diagnoses the parasitic oscillations of the two converters.
3 self-test self-diagnostic software
The self-diagnostic software of the intelligent measuring instrument can be “state table check method†“check fault dictionary†or a higher level “expert systemâ€. "Distributed Fault Tolerant Systems" have also been adopted in measuring instruments. The program operation monitoring system (watchdog watchdog) is also in the scope of self-test. Common methods include knowledge storage, various inference mechanisms, forward reasoning, inexact reasoning, and search strategies. For example, in the processing of self-calibration and self-compensation of sensitive components by a single-chip microcomputer, there are two processes of learning and normal measurement. The learning process is divided into a compensation (zero) learning program, a gain (sensitivity) learning program, and a calibration learning program. Here, a table lookup method is commonly used to process and correct data. These programs have an entry address, such as TEST0, TEST1, which lists these entry addresses as a table, then uses the sequence number of the test program as an offset, and uses the table driver to call the self-test subroutine one by one. Each self-test subroutine of a general intelligent measuring instrument has a corresponding flag TFi. When the subroutine detects a problem, the corresponding TFi is set to 1, otherwise it is set to 0. The total fault flag of the meter is TF0. A simple flow chart of the self-test of the measuring instrument.
4 Conclusion
The self-test self-diagnosis system of the intelligent measuring instrument is a very complicated system, which includes hardware modules and software programs. Different measuring objects of different instruments have their own unique methods and methods. Currently they still use incentives - in response to the "loopback" self-test mode. The continuous development and improvement of the "expert system" will definitely make a qualitative change in the self-test and self-diagnosis system of the measuring instrument.
Rechargeable Mosquito Killer,Mosquito Swatter Racket,Portable Mosquito Killer Lamp
Guangdong Dp Co., Ltd. , https://www.dp-light.com