Misalignment and unbalance fault severity estimation using stator current measurements

Corne, Bram; Knockaert, Jos; Desmet, Jan

doi:10.1109/demped.2017.8062363

Cited by 11 publications

(5 citation statements)

References 24 publications

(31 reference statements)

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“…Verma and Kolekar applied a Fast Fourier Transform (FFT) to the stator current to detect shaft misalignment and showed [10]. Corne et al used spectral and wavelet analysis of stator currents to detect angular misalignment in permanent magnet synchronous motor drive systems and demonstrated which multiplicities of the failure frequency are the most sensitive to misalignment and the least sensitive to changes in motor operating conditions [31]. Goktas et al showed that a multilayer perceptron-based machine learning algorithm using vibration and stator current has an excellent performance in the automatic detection of parallel misalignment fault [32].…”

Section: Literatur Reviewmentioning

confidence: 99%

Misalignment detection on linear feed axis using sensorless motor current signals

Demetgul

Zihan

Heider

et al. 2023

Int J Adv Manuf Technol

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Due to ageing populations and a shortage of skilled labour, automatic machine condition monitoring is a powerful tool to ensure smooth operation of production systems with reduced manpower. Automatic condition monitoring enables early detection of machine faults, greatly increasing uptime, reliability, and safety. However, conventional fault detection methods based on vibration require installation of additional sensors, thus bringing up implementation effort and initial costs. The linear feed axis is a machine component whose failure can bring an entire production line to a standstill. Therefore, this study presents a sensorless approach, which uses a linear axis' motor current for the detection of misalignment. Motor current time series data was encoded as images and then fed to a CNN, more precisely a revised residual neural network (ResNet). A random search hyper-parameter tuning technique was used to optimise the structure of the CNN. Then, transfer learning was used to apply the current signal features already learned to other scenarios. The results showed that both horizontal and vertical misalignments of linear feed axes can be well detected by a revised ResNet using motor current signals, with an accuracy of 99.76%. Using transfer learning, misalignments were detected with an accuracy of 92.67% -even under the in uence of external forces.

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Section: Literatur Reviewmentioning

confidence: 99%

Misalignment detection on linear feed axis using sensorless motor current signals

Demetgul

Zihan

Heider

et al. 2023

Int J Adv Manuf Technol

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“…There are many approaches for condition monitoring of components through investigations of the motor current [26]. These range from wear detection of motors and bearings to characterisation [14,27] and system property change monitoring [28] he different system properties can result from geometrical differences in the components or different forms of wear [14,29]. Research results in condition monitoring by the motor current of ball screws drives (BSD) will be reviewed in depth.…”

Section: Current-based Component Wear Detectionmentioning

confidence: 99%

Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis

Ströbel,

Bott,

Wortmann

et al. 2023

Machines

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In today’s manufacturing landscape, Digital Twins play a pivotal role in optimising processes and deriving actionable insights that extend beyond on-site calculations. These dynamic representations of systems demand real-time data on the actual state of machinery, rather than static images depicting idealized configurations. This paper presents a novel approach for monitoring tool and component wear in CNC milling machines by segmenting and classifying individual machining cycles. The method assumes recurring sequences, even with a batch size of 1, and considers a progressive increase in tool wear between cycles. The algorithms effectively segment and classify cycles based on path length, spindle speed and cycle duration. The tool condition index for each cycle is determined by considering all axis signals, with upper and lower thresholds established for quantifying tool conditions. The same approach is adapted to predict component wear progression in machine tools, ensuring robust condition determination. A percentage-based component state description is achieved by comparing it to the corresponding Tool Condition Codes (TCC) range. This method provides a four-class estimation of the component state. The approach has demonstrated robustness in various validation cases.

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“…It must be noticed that detecting a 'small' misalignment does not imply that the machine is defective; whereas it is a sign of excessive loads that can easily lead to premature failure. Consequently, companies impose their own acceptable tolerance for judging the severity of misalignment [16].…”

Section: Misalignmentmentioning

confidence: 99%

Experimental study on energy consumption in rotating machinery caused by misalignment

Abouelanouar¹,

Elkihel

Gziri

2020

SN Appl. Sci.

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In this study we present an evaluation of the energy consumption in the case of shaft misalignment. Misalignment is one of the most common defects in rotating machinery that causes noticeable losses of energy. The proposed methodology focuses on finding a correlation between vibration level and energy consumption for different degrees of misalignment. In fact, the monitoring of energy consumption became a necessity for industries for the implementation of the appropriate maintenance strategy. In order to achieve this, vibration analysis is a measurement tool used to detect and diagnose rotating machinery faults, such as misalignment, unbalance and bearing defects. The results of this experimental investigation show that the rate of energy consumption depends highly on the degree of shaft misalignment, especially, in the case of parallel misalignment. Also, vibration analysis has been proven to be an effective tool to early detect misalignment shaft and monitoring electrical energy consumption.

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Misalignment and unbalance fault severity estimation using stator current measurements

Cited by 11 publications

References 24 publications

Misalignment detection on linear feed axis using sensorless motor current signals

Misalignment detection on linear feed axis using sensorless motor current signals

Monitoring of Tool and Component Wear for Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly Detection and Wear Cycle Analysis

Experimental study on energy consumption in rotating machinery caused by misalignment

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