locates the previous day most similar to the current circumstances and uses the
historical information from that day as a predictor. In this case the prediction
is treated as an additive correction to the static load model. To guard against
outliers, the eight most similar days are located and their additive corrections
averaged. A database was constructed of temperature, humidity, wind speed,
and cloud cover at three local weather centers for each hour of the 15-year
historical record, along with the difference between the actual load and that
predicted by the static model. A linear regression analysis was performed to
determine the relative effects of these parameters on load, and the coefficients
were used to weight the distance function used to locate the most similar days.
The resulting system yielded the same performance as trained human fore-
casters but was far quicker—taking seconds rather than hours to generate a daily
forecast. Human operators can analyze the forecast’s sensitivity to simulated
changes in weather and bring up for examination the “most similar” days that
the system used for weather adjustment.
Diagnosis
Diagnosis is one of the principal application areas of expert systems. Although
the handcrafted rules used in expert systems often perform well, machine learn-
ing can be useful in situations in which producing rules manually is too labor
intensive.
Preventative maintenance of electromechanical devices such as motors and
generators can forestall failures that disrupt industrial processes. Technicians
regularly inspect each device, measuring vibrations at various points to deter-
mine whether the device needs servicing. Typical faults include shaft misalign-
ment, mechanical loosening, faulty bearings, and unbalanced pumps. A
particular chemical plant uses more than 1000 different devices, ranging from
small pumps to very large turbo-alternators, which until recently were diag-
nosed by a human expert with 20 years of experience. Faults are identified by
measuring vibrations at different places on the device’s mounting and using
Fourier analysis to check the energy present in three different directions at each
harmonic of the basic rotation speed. This information, which is very noisy
because of limitations in the measurement and recording procedure, is studied
by the expert to arrive at a diagnosis. Although handcrafted expert system rules
had been developed for some situations, the elicitation process would have to
be repeated several times for different types of machinery; so a learning
approach was investigated.
Six hundred faults, each comprising a set of measurements along with the
expert’s diagnosis, were available, representing 20 years of experience in the
field. About half were unsatisfactory for various reasons and had to be discarded;
the remainder were used as training examples. The goal was not to determine
1.3 FIELDED APPLICATIONS 25