Patent ID: 11874328
Assignee: BTECH INC.
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 9:
10. A system for training a neural net to automatically detect when a battery changeout occurs, said system comprising:
a) an input device;
b) said neural net comprising:
i) one or more nodes; and
ii) one or more weighting factors associated with one or more inputs to each of said one or more nodes;

c) an output device;
d) a digital processor; and
e) a permanent memory comprising computer readable instructions to physically cause said digital processor to perform the steps:
i) read in by said input device, training battery data from a training battery monitor monitoring a training battery wherein said training battery data is read in once per about a data period;
ii) format said training battery data as a training battery data point;
iii) store said training battery data point as an element of a training battery data vector wherein said training battery data vector additionally comprises N−1 prior sequential training battery data points, and wherein said training battery data vector includes at least one training battery data point corresponding to a time before said training battery changeout has occurred and at least one training battery data point corresponding to a time after said training battery changeout has occurred;
iv) format a training output vector of N elements wherein:
1. Each of said elements of said training output vector corresponds to a sequential data period; and
2. said training output vector indicates if and when a training battery changeout has occurred during one of said sequential data periods and includes an indication of whether or not said training battery changeout was due to a premature failure of said training battery or a scheduled changeout of said training battery;

v) train said neural net to distinguish between a training changeout due to a premature failure of said training battery or a scheduled changeout of said training battery;
vi) read said training battery data vector and said training output vector into said neural net;
vii) train said neural net such that a measure of the error between said training output vector and an output vector of said neural net is minimized by varying said weighting factors; and
viii) output by said output device said trained neural net.