Early detection of stator voltage imbalance in three-phase induction motors

Samsi, Rohan; Ray, Asok; Mayer, Jeffrey S.

doi:10.1016/j.epsr.2008.06.004

Cited by 24 publications

(11 citation statements)

References 21 publications

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“…Computationally, these methods are less expensive than other existing and can detect faults in an early stage. In the same vein, monitoring fatigue damage has been studied [65].…”

Section: Wavelet Transform Applicationsmentioning

confidence: 99%

Maintenance Management Based on Signal Processing

Márquez¹,

Carrato²,

Pérez³

et al. 2013

Digital Filters and Signal Processing

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Most of the wind turbines are three-blade units (Figure 1.) [55]. Once the wind drives the blades, the energy is transmitted via the main shaft through the gearbox (supported by the bearings) to the generator. The generator speed must be as near as possible to the optimal for the generation of electricity. At the top of the tower, assembled on a base or foundation, the housing or nacelle is mounted and the alignment with the direction of the wind is controlled by a yaw system. There is also a pitch system in each blade. This mechanism controls the wind power and sometimes is employed as an aerodynamic brake. The wind turbine features a hydraulic brake to stop itself when it is needed. Finally, there is a meteorological unit that provides information about the wind (speed and direction) to the control system. 1.1. Maintenance in Wind Turbines Maintenance is a key tool to ensure the operation of all components of a set. One of the objectives is to use available resources efficiently. The classical theory of maintenance was focused on the corrective and preventive maintenance [9] but alternatives to corrective and preventive maintenance have appeared in recent years. One of them is Condition Based Maintenance, which ensures the continuous monitoring and inspection of the wind turbine detecting emerging faults and organizing maintenance tasks that anticipate the failure [59]. Condition Based Maintenance implies acquisition, processing, analysis and interpretation of data and the selection of proper maintenance actions. This is achieved using condition monitoring systems [27, 28]. Thereby, CBM is presented as a useful technique to improve not only the maintenance but the safety of the equipments. Byon and Ding [14] or McMillan and Ault [50] have demonstrated its successful application in wind turbines, making the CBM

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“…Computationally, these methods are less expensive than other existing and can detect faults in an early stage. In the same vein, monitoring fatigue damage has been studied [65].…”

Section: Wavelet Transform Applicationsmentioning

confidence: 99%

Maintenance Management Based on Signal Processing

Márquez¹,

Carrato²,

Pérez³

et al. 2013

Digital Filters and Signal Processing

View full text Add to dashboard Cite

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“…Inaccurate parameters of a physical model can significantly affect the efficiency of model based early detection of small slowly varying faults. For the purpose of developing a data-driven small fault detection method, many necessary techniques have been used, such as a filter-based method, exponential weighted moving average (EWMA) based method and a cumulative sum (CUSUM) based method [ 23 , 24 , 25 , 26 , 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults

Zhou

Park

Wen

et al. 2018

Sensors

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Early detection of slowly varying small faults is an essential step for fault prognosis. In this paper, we first propose an average accumulative (AA) based time varying principal component analysis (PCA) model for early detection of slowly varying faults. The AA based method can increase the fault size as well as decrease the noise energy. Then, designated component analysis (DCA) is introduced for developing an AA-DCA method to diagnose the root cause of the fault, which is helpful for the operator to make maintenance decisions. Combining the advantage of the cumulative sum (CUSUM) based method and the AA based method, a CUSUM-AA based method is developed to detect faults at earlier times. Finally, the remaining useful life (RUL) prediction model with error correction is established by nonlinear fitting. Once online fault size defined by detection statistics is obtained by an early diagnosis algorithm, real-time RUL prediction can be directly estimated without extra recursive regression.

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“…It has been developed to help designers generate a state machine diagram and convert it to industrial sequence control programs for various industrial automation projects. The wavelet transform was utilised to partition a time series data that maintains the essential behaviour of the dynamical system while reducing the complexity of the problem from the continuous domain to the discrete domain, hence reducing the problem to a finite state representation (16) . This represents an essential feature of space partitioning that enables pattern classification.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring of pulsed electric field food processing systems

Bastaki¹,

Gaouda²,

El-Hag³

et al. 2012

Insight

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This paper proposes a novel automated monitoring and control tool that integrates a finite state machine and windowing wavelet-based technique for pulsed electric field food processing units. The proposed tool uses the waveletbased technique to partition voltage waveforms captured across a test cell into a series of events. A pair of proposed finite state machines utilises these events to monitor both arcing activities in the processed liquid and variations in the medium temperature. Abnormal activities are detected and classified in a way that enables control of the applied pulses to prevent undesired flashover that could damage the processed food or drive its temperature into a pasteurisation temperature. The automation features of the proposed technique are extracted from real data of orange juice, apple juice and water in pulsed electric field food processing units.

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Early detection of stator voltage imbalance in three-phase induction motors

Cited by 24 publications

References 21 publications

Maintenance Management Based on Signal Processing

Maintenance Management Based on Signal Processing

Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults

Condition monitoring of pulsed electric field food processing systems

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