Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms

Mohanraj, T.; Yerchuru, Jayanthi; Krishnan, H. Anjana; Aravind, R.; Yameni, R.

doi:10.1016/j.measurement.2020.108671

Cited by 124 publications

(52 citation statements)

References 42 publications

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“…Figure 6 shows performance graph of the developed NN estimator. The 75 % of data (1050 samples) were utilized for training purpose and 25 % (450 samples) were used for testing and validation purpose (Mohanraj et al, 2021b).…”

Section: Figure 4 Resultant Vibration Signatures For Different Toolsmentioning

confidence: 99%

Decision support system for tool condition monitoring in milling process using artificial neural network

Mohanraj

Tamilvanan²

2021

JER is an international, peer-reviewed journal that publishes f

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This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.

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Section: Figure 4 Resultant Vibration Signatures For Different Toolsmentioning

confidence: 99%

Decision support system for tool condition monitoring in milling process using artificial neural network

Mohanraj

Tamilvanan²

2021

JER is an international, peer-reviewed journal that publishes f

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“…Statistical features and Hoelder's exponent were derived from WT coefficients for milling tool health state monitoring. Here, the HIs were the input for an SVM and Decision Tree classifier [25]. HIs were derived by a convolutional neural network for milling tool condition monitoring based on the wavelet decomposition in [26].…”

Section: Time-frequency-based Health Indicatorsmentioning

confidence: 99%

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Nentwich

Reinhart

2021

Robotics

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Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.

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“…A three-layered ANN having 10 neurons in each layer is designed in the present work. Two types of ANN are designed and tested in the present work using cascaded forward backdrop and feed-forward backdrop-based designs, as shown in Figure 5a,b, respectively [14,27,29,31,[40][41][42][43][44][45]. ANN training is done using four different algorithms: Bayesian Regulation, Polak-Ribiere Restarts, Gradient Descent with momentum and adaptive learning rate, and finally, Levenberg Marquardt algorithm.…”

Section: Ann-based Analysismentioning

confidence: 99%

ANN-Based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation

Sinha¹,

Hati

Benbouzid

et al. 2021

Machines

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The requisite of direct-on-line (DOL) starting for various applications in underground mines subjects the rotor bars of heavy-duty squirrel cage induction motors (SCIMs) to severe stresses, resulting in sustained fault in the rotor bars, unlike the applications where mostly reduced voltage starting is preferred. Furthermore, SCIMs working in underground mines are also affected by unforeseen frequency fluctuations. Hence, the paper proposes a discrete wavelet transform (DWT)-based broken rotor bar detection scheme using the stator current analysis of SCIM when subjected to a frequency regulation (±4% of 50 Hz supply) in steady-state, as prevalent in underground mines. In this regard, the level-seven detailed coefficient obtained by the DWT-based multi-resolution analysis of stator current corresponding to the healthy rotor is compared with that of the faulty rotor to extract the necessary features to identify the fault. Further implementation of the proposed scheme is done using artificial neural network (ANN)-based pattern recognition techniques, wherein both feed-forward backdrops and cascaded forward backdrop type ANNs have been used for fault pinpointing based on the feature extraction results obtained from DWT. The scheme is developed and analysed in MATLAB/Simulink using 5.5 kW, 415 V, 50 Hz SCIM, which is further validated using the LabVIEW-based real-time implementation.

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Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms

Cited by 124 publications

References 42 publications

Decision support system for tool condition monitoring in milling process using artificial neural network

Decision support system for tool condition monitoring in milling process using artificial neural network

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

ANN-Based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation

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