An FFT-based high-speed spindle monitoring system for analyzing vibrations

Mo, Yu-Ching; Su, Ke-Yu; Kang, Wei; Chen, Liang-Bi; Chang, Wan-Jung; Liu, Yunhui

doi:10.1109/icsenst.2017.8304429

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…9. In the equation; Sx(f) is the output of the Fourier transform in the frequency domain, x(t), the function due to time and the frequency in radians 2πf [27].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

Güvenç

Bilgiç

Mıstıkoğlu

2021

Preprint

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In recent years, with the development of sensor technologies, communication platforms, cyber physical systems, storage technologies, internet applications and controller infrastructures, the way has been opened to produce competitive products with high quality and low cost. In turning, which is one of the important processes of machining, chatter vibrations are among the biggest problems affecting product quality, productivity and cost. There are many techniques proposed to reduce chatter vibrations for which the exact cause cannot be determined. In this study, an active vibration control based on Sliding Mode Control (SMC) has been implemented in order to reduce and eliminate chatter vibration, which is undesirable for the turning process. In this context, three-axis acceleration data were collected from the cutting tool during the turning of Ti6Al4V. Finite Impulse Response (FIR) filtering, Fast Fourier Transform (FFT) analysis and integral process were carried out in order to use the raw acceleration data collected over the system in control. The system was modeled mathematically and an active control block diagram was created. It was observed that chattering decreased significantly after the application of active vibration control. The surface quality formed by the amplitude of the graph obtained after active control has been compared and verified with the data obtained from the actual manufacturing result.

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“…9. In the equation; Sx(f) is the output of the Fourier transform in the frequency domain, x(t), the function due to time and the frequency in radians 2πf [27].…”

Section: Fast Fourier Transform (Fft)mentioning

confidence: 99%

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

Güvenç

Bilgiç

Mıstıkoğlu

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As a comparison, an analysis of the fan data utilizing current frequency domain monitoring practices was conducted [8,[10][11][12]. Many of these methods are similar in nature whereby vibration measurements are converted into the frequency domain and the peaks with the largest magnitude are monitored over time.…”

Section: Condition Monitoring Techniquesmentioning

confidence: 99%

“…As a result, designers raise the alarm thresholds on peak responses. Consequently, if there is a new degradation mode, the degradation is usually severely underway, or the machine is dead before any alarm can be issued [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…A secondary hindrance is that vibration measurements are inherently of a high frequency, with sample rates for the vibrations being in the kHz range. The glut of data generated by these sensors quickly becomes unmanageable for storage, which makes vibration-based monitoring untenable [12,[20][21][22]. Thus, vibration resonance spectrometry (VRS) has been proposed to process the data for the loss-less dimension reduction of the signals and produce a multivariate dataset from one vibration sensor that can be consumed by a ML model.…”

Section: Introductionmentioning

confidence: 99%

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Vibration Resonance Spectrometry (VRS) for the Advanced Streaming Detection of Rotor Unbalance

et al. 2023

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Determination of the diagnosis thresholds is crucial for the fault diagnosis of industry assets. Rotor machines under different working conditions are especially challenging because of the dynamic torque and speed. In this paper, an advanced machine learning based signal-processing innovation termed the multivariate state estimation technique is proposed to improve the accuracy of the diagnosis thresholds. A novel preprocessing technique called vibration resonance spectrometry is also applied to achieve a low computation cost capability for real-time condition monitoring. The monitoring system that utilizes the above methods is then applied for prognostics of a fan model as an example. Different levels of radial unbalance were added on the fan and tested, and then compared with the health state. The results show that the proposed methodology can detect the unbalance with a good accuracy and low computation cost. The proposed methodology can be applied for complex engineering assets for better predictive monitoring that could be processed with on-premise edge devices, or eventually a cloud platform due to its capacity for loss-less dimension reduction.

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“…Therefore, most researchers focused on FFT to detect damage in pipes [12]. Also for structural health monitoring(SHM) and in frequency domain analysis of acceleration measurements [14][15][16]. FFT is an improved algorithm for the performance of a Discrete Fourier Transform (DFT), which is an effective technique for static signal analysis [16].…”

Section: Analysis In Frequency Domainmentioning

confidence: 99%

PCA Classification of vibration signals in WSN based oil pipeline monitoring system

Shareef

Mossa

2019

J. Al-Qadisiyah Comp. Sci. Math.

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Using wireless sensor network technology in structure health monitoring applications results in generating large amount of data. To sift through this data and extract useful information an extensive data analysis should be applied. In this paper, a Wireless Sensor Network (WSNs) is proposed for the oil pipeline monitoring system with proposed method for event detection and classification. The method depends on the Principal Component Analysis (PCA). It applied to features extracted from vibration signals of the monitored pipeline. These vibration signals are collected while applying damage events (knocking and drilling) to the oil pipeline. PCA is applied to features extracted from both time domain and frequency domain. The results manifest that this method is able to detect the existence of damage and also to distinguish between the different levels of harmful events applied to the pipeline.

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An FFT-based high-speed spindle monitoring system for analyzing vibrations

Cited by 8 publications

References 5 publications

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

WITHDRAWN: Identification of Chatter Vibrations and Active Vibration Control by Using Sliding Mode Controller on Dry Turning of Ti6al4v

Vibration Resonance Spectrometry (VRS) for the Advanced Streaming Detection of Rotor Unbalance

PCA Classification of vibration signals in WSN based oil pipeline monitoring system

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