SELF-MONITORING STORAGE DEVICE USING NEURAL NETWORKS

A digital data storage device such as a rotating magnetic disk drive contains an on-board condition monitoring system, comprising a neural network coupled to multiple inputs derived from measured parameters of disk drive operation. The neural network uses a configurable set of weights to compute one or more quantities representing disk drive condition as a function of the various inputs. The weights are stored in a configuration table, which can be overwritten by a host computer. The drive is sold and installed with one set of weights, based on the then existing knowledge of the disk drive designers, and may be updated in the field as the designers acquire experience data by simply writing the weights to the configuration table of the disk drive, without altering disk drive control code or other disk drive features. Preferably, the disk drive designers include as input to the neural network any parameter which might conceivably be useful, even if the designers initially believe that the parameter has no significance. In this case, the designers can assign the parameter a weight of zero during initial release. If subsequent experience then shows that the parameter has some unexpected significance, the neural network can be corrected simply by changing weighting factors, without altering the control programming code.