Rotor Asymmetries Faults Detection in Induction Machines Under the Impacts of Low-Frequency Load Torque Oscillation

Marzebali, Mohammad Hoseintabar; Bazghandi, Reza; Abolghasemi, Vahid

doi:10.1109/tim.2022.3201950

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…First, we show the time-varying single-component signal as follows: (9) where 𝐴(𝑡) is the maximum amplitude of the signal and ϕ(t) shows the phase of the signal. Now, the STFT of the above signal can be written as follows: (10) where 𝑤(𝑢) is the window function.…”

Section: Multi-synchro-squeezing Transformmentioning

confidence: 99%

“…Consequently, timely and prompt detection of faults occurred in the machine is essential to ensure IMs' safety and operational efficiency. Various diagnostic techniques were reported in the past to ensure the optimal performance of the induction machines and guarantee the safety of IMs such as motor current signature analysis (MCSA) [9,10], vibration analysis [11,12], stray flux signals [12,13], deep learning [14], and acoustic emission analysis [15]. Among mentioned strategies, the MCSA method has attracted more attentions due to simplicity, non-invasive nature and costeffectiveness.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Multi-Synchro-Squeezing Transform for Fault Diagnosis in Induction Machine Based on Third-Order Energy Operator of Stator Current Signature

Bazghandi,

Marzebali,

Abolghasemi

2024

IEEE Access

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In traditional motor current signature analysis (MCSA) approach, the spectral leakage of the fundamental supply frequency can obscure the characteristic fault component under low-load or no-load conditions. Furthermore, most time-frequency (TF) methods often have low resolution and are not qualified to produce a narrow band in the output. In this paper, we employ multi-synchro-squeezing transform (MSST) to show its effectiveness in fault detection of induction machines, for the first time. The key innovation of this work is merging MSST (due to its high time-frequency resolution) with the third-order energy operator (TOEO) (due to its high accuracy in fault detection). Specifically, TOEO is used to overcome the leakage effects of the supply frequency, through a demodulation approach for asymmetric fault detection along with MSST technique. The proposed method was evaluated for induction machine fault detection in both steadystate and transient conditions. Both analytical and experimental results confirm that the proposed method can excellently reveal the fault characteristic frequency in steady-state and transient mode, instead of the sideband frequencies.

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Section: Multi-synchro-squeezing Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Synchro-Squeezing Transform for Fault Diagnosis in Induction Machine Based on Third-Order Energy Operator of Stator Current Signature

Bazghandi,

Marzebali,

Abolghasemi

2024

IEEE Access

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“…In addition, some researchers combined the two methods for fault detection. To achieve rotor asymmetric fault detection, Marzebali et al proposed a method of mapping the static reference system obtained by the single stator current and Hilbert transform to a two-axis rotating reference system, and then used the synchronous squeezing wavelet transform for the time-frequency analysis of fault stator current under transient conditions [26]. Benninger et al proposed analytical modeling based on a multi-coupled circuit model and a feedforward neural network for IM fault detection [27].…”

Section: Introductionmentioning

confidence: 99%

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

Zhong

Wang

2023

Meas. Sci. Technol.

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To accurately and sensitively track the stator current of the induction motor (IM) and detect faults, stochastic resonance (SR) and Teager energy operator (TEO) are combined to detect the fault in the residual stator current of sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. Firstly, a new approach law is constructed to establish an SMO for better state tracking. Secondly, SR is used to absorb noise and amplify the detection residual of the SMO, and the output results are estimated by TEO in the time domain to achieve fault detection. Finally, the detection results of IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of stator current is processed by SR and TEO, and the effectiveness of fault detection is enhanced.

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“…On the other hand, IMs have significant advantages over synchronous and DC machines, such as lower cost, simplicity and robustness [2]. Due to the operator's safety and the widespread use of this type of machine in several industries, condition monitoring and fault diagnosis in IMs are very important and inevitable [3][4][5][6]. Generally, IMs are one of the industry bases, and the use of these machines is necessary for many factories.…”

Section: Introductionmentioning

confidence: 99%

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

et al. 2023

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Condition monitoring of induction machines (IMs) with the aim of increasing the machine’s lifetime, improving the efficiency and reducing the maintenance cost is necessary and inevitable. Among different types of methods presented for mechanical and electrical fault tracing in induction machines, stator current signature analysis has attracted great attention in recent decades. This popularity is mainly due to the non-invasive nature of this technique. A non-recursive method named variational mode decomposition (VMD) is used for the decomposition of any signal into several intrinsic mode functions (IMFs). This technique can be employed for detection of faulty components in a current signature. However, mode mixing of extracted IMFs makes the mechanical and electrical fault detection of IMs complicated, especially in the case where fault indices emerge close to the supply frequency. To achieve this, we rectify the signal of stator current prior to applying VMD. The main advantage of the presented approach is allowing the fault indices to be properly demodulated from the main frequency to avoid mode mixing phenomenon. The method shows that the dominant frequencies of the current signal can be isolated in each IMFs, appropriately. The proposed strategy is validated to detect the rotor asymmetric fault (RAF) in a wound rotor induction machine (WRIM), in both transient and steady-state conditions.

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Rotor Asymmetries Faults Detection in Induction Machines Under the Impacts of Low-Frequency Load Torque Oscillation

Cited by 10 publications

References 31 publications

Enhanced Multi-Synchro-Squeezing Transform for Fault Diagnosis in Induction Machine Based on Third-Order Energy Operator of Stator Current Signature

Enhanced Multi-Synchro-Squeezing Transform for Fault Diagnosis in Induction Machine Based on Third-Order Energy Operator of Stator Current Signature

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

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