US 12,688,402 B2Granted

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

AssigneeTata Consultancy Services Limited

InventorsSaurabh Sahu, Kriti Kumar, Achanna Anil Kumar, Mariswamy Girish Chandra, Angshul Majumdar

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Abstract

Existing Convolutional Dictionary Learning (CDL) based machine fault classification do not utilize label information while learning the dictionary, hence the representation learned are not class-discriminative. Method and system disclosed herein provide a label-consistent convolutional dictionary learning approach for machine fault classification. The approach involves generating a training data for a classifier, wherein coefficients forming a plurality of class-discriminative features form the training data. The training data is then used to train a classifier, which is then used to perform machine fault classification for a given test data.

Claims12

Dependent claims

A processor implemented method, comprising:

receiving, via one or more hardware processors, 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, wherein the joint optimization is represented as
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where, Q∈RC×K represents one hot encoded classification labels for C-classes, and K is the total number of training samples;
generating, via the one or more hardware processors, 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);
concatenating, via the one or more hardware processors, 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, wherein the classifier is trained using the concatenated matrix of coefficients forming the plurality of class-discriminative features and the set of classification labels;
and
using, via the one or more hardware processors, the trained classifier for performing machine fault classification.

91 more elements

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, wherein the joint optimization is represented as
arg
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min
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where, Q∈RC×K represents one hot encoded classification labels for C-classes, and K is the total number of training samples;
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);
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, wherein the classifier is trained using the concatenated matrix of coefficients forming the plurality of class-discriminative features and the set of classification labels;
and
use the trained classifier for performing machine fault classification.

95 more elements

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, wherein the joint optimization is represented as
arg
⁢
min
{
D
m
}
⁢
{
X
m
}
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{
W
m
}
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1
2
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∑
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=
1
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2
+
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m
=
1
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1
+
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F
2
s
.
t
.

D
m

2
=
1
∀
m
,
where, Q∈RC×K represents one hot encoded classification labels for C-classes, and K is the total number of training samples;
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);
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, wherein the classifier is trained using the concatenated matrix of coefficients forming the plurality of class-discriminative features and the set of classification labels;
and
using the trained classifier for performing machine fault classification.

91 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/793,331Filed 2 Aug 2024
Priority: IN 20232105660923 Aug 2023
Published: US 2026/0079802 A119 Mar 2026
Granted: 21 Jul 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]
G06F 11/2252
Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing: using fault…
G06F 11/2257
Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing: using expert…
G06N 5/04
Computing arrangements using knowledge-based models: inference or reasoning models