Method and system of machine fault classification using label-consistent convolutional dictionary learning

A system, comprising:

• one or more hardware processors;
• a communication interface;
• and
• a memory storing a plurality of instructions, wherein the plurality of instructions cause the one or more hardware processors to:
• receive a training sensor data (S) and a set of classification labels (Q) associated with the training sensor data, as input data;
• perform a joint optimization on the input data to learn a plurality of parameters comprising a) one or more dictionary atoms {Dm}, one or more coefficients {Xm} associated with each of the one or more dictionary atoms, and weights {Wm} associated with a label consistency term for each of the one or more coefficients for mth filter in a sequence of filters, wherein performing the joint optimization comprises iteratively updating the parameters {Dm}, {Xm}, and {Wm}, till an objective function represented in the joint optimization is converged, wherein a plurality of class-discriminative dictionaries are learnt for each of a plurality of filters, by performing the joint optimization;
• generate a plurality of coefficients forming a plurality of class-discriminative features, from the plurality of learnt class-discriminative dictionaries and the training sensor data (S);
• and
• concatenate the plurality of coefficients of the plurality of filters to obtain a concatenated matrix of coefficients wherein the concatenated matrix of coefficients form a training data for a classifier.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a training sensor data (S) and a set of classification labels (Q) associated with the training sensor data, as input data;
• performing, via the one or more hardware processors, a joint optimization on the input data to learn a plurality of parameters comprising a) one or more dictionary atoms {Dm}, one or more coefficients {Xm} associated with each of the one or more dictionary atoms, and weights {Wm} associated with a label consistency term for each of the one or more coefficients for mth filter in a sequence of filters, wherein performing the joint optimization comprises iteratively updating the parameters {Dm}, {Xm}, and {Wm}, till an objective function represented in the joint optimization is converged, wherein a plurality of class-discriminative dictionaries are learnt for each of a plurality of filters, by performing the joint optimization;
• generating a plurality of coefficients forming a plurality of class-discriminative features, from the plurality of learnt class-discriminative dictionaries and the training sensor data (S);
• and
• concatenating the plurality of coefficients of the plurality of filters to obtain a concatenated matrix of coefficients wherein the concatenated matrix of coefficients form a training data for a classifier.