Method for Processing Three Kinds of Errors of 2 Ton Electronic Scales in Shanghai

Electronic scales have high-precision analog-to-digital conversion technology and can be used for both AC and DC applications. Meters can also be used in automotive floor scales. The utility model has the advantages of simple structure, stable performance, convenient installation and maintenance, high precision and good long-term stability. Applicable to metallurgy, chemical industry, railways, ports, and industrial and mining enterprises to measure various trucks and goods.

Electronic scales are industrial weighing tools, often used in the automotive industry, logistics industry, factory industry, construction and other fields, the range of the product range is not limited, a small range of up to 1 ton of electronic platform scale, the product structure is simple, a Large bench top, can be used in harsh environments.

There are also errors in weighing items when electronically weighing scales. This error is generally divided into system error, random error, and gross error. The system error mainly comes from the weight error contained in the electronic scale itself, and the random error may be due to the length of work. The time causes the error to increase, how should we deal with these three kinds of errors?

Today we will briefly introduce the treatment of these three kinds of errors.

System error handling method: elimination or compensation of metal analyzer system errors: The nature of the system error determines that it cannot be eliminated by increasing the number of measurements. The detection system can be perfected, eliminated or compensated for by systematic measures such as selecting a better detection system and increasing the operating level.

Random error processing method: the reduction or elimination of random errors: According to the characteristics of random errors obey statistical rules, multiple measurements and statistical processing methods can be used to reduce or eliminate random errors.

Coarse error processing method: removal of gross errors: data containing errors in data processing should be removed. Testers should be serious and avoid mistakes.

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