Production Monitoring and Data Mining - No Strip Mining Allowed!

Overview:

Does your manufacturing or process plant have many sources of production data that is or has become voluminous? There are a variety of ways data can be mined, including manually, or with increasingly sophisticated analysis software that includes artificial intelligence and neural network modeling tools. This paper will discuss these possibilities.

Any manufacturing or process plant has many sources of production data, and that data, if kept, very soon becomes voluminous. If a facility has 1000 sensors on the plant floor, and data is taken at once a second interval from all those sensors, that's 1000 data points per second, 60,000 data points per minute, and over 86 million data points per day. Most plants have many more than 1000 sensors that can provide data to a data historian.

The concept of data mining is similar to finding a needle in a haystack. In fact, noted manufacturing process consultant Eliyahu Goldratt titled his 1991 book about data mining "The Haystack Syndrome," because he had discovered that traditional ways of mining data were about as effective as trying to find that needle.

In essence, data mining is the process of sifting historical data to find data that supports a premise,
or produces a pattern. The implication is that only favorable data is discovered, making the process somewhat dubious. There are a variety of ways data can be mined, including manually, or with increasingly sophisticated analysis software that includes artificial intelligence and neural network modeling tools.

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