This article presents a novel computational method for the diagnosis of broken rotor bars in thre... more This article presents a novel computational method for the diagnosis of broken rotor bars in three phase
asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is
applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient
because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s
current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially
utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken
bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two
schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed
schemes is evaluated by multiple experimental test cases. The results obtained indicate that the sug-
gested approaches based on the combination of PCA and HMMs, can be successfully utilized not only
for identifying the presence of a broken bar but also for estimating the severity (number of broken bars)
of the fault.
This article presents a novel computational method for the diagnosis of broken rotor bars in thre... more This article presents a novel computational method for the diagnosis of broken rotor bars in three phase
asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is
applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient
because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s
current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially
utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken
bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two
schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed
schemes is evaluated by multiple experimental test cases. The results obtained indicate that the sug-
gested approaches based on the combination of PCA and HMMs, can be successfully utilized not only
for identifying the presence of a broken bar but also for estimating the severity (number of broken bars)
of the fault.
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asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is
applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient
because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s
current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially
utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken
bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two
schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed
schemes is evaluated by multiple experimental test cases. The results obtained indicate that the sug-
gested approaches based on the combination of PCA and HMMs, can be successfully utilized not only
for identifying the presence of a broken bar but also for estimating the severity (number of broken bars)
of the fault.
asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is
applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient
because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s
current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially
utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken
bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two
schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed
schemes is evaluated by multiple experimental test cases. The results obtained indicate that the sug-
gested approaches based on the combination of PCA and HMMs, can be successfully utilized not only
for identifying the presence of a broken bar but also for estimating the severity (number of broken bars)
of the fault.