Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling

Patange, Abhishek D.; Jegadeeshwaran, R.

doi:10.36001/ijphm.2020.v11i2.2929

Cited by 26 publications

(14 citation statements)

References 41 publications

(42 reference statements)

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“…The primary objective of this system is to acquire wheel hub vibration and secondarily to store the data for analyzing trend of response. An open source low-priced microcontroller Arduino Mega was interfaced with an accelerometer ADXL335 and communicated to Microsoft Excel to accumulate & exhibit the acquired readings [25][26][27].…”

Section: In-house Design and Development Of Daqmentioning

confidence: 99%

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Pardeshi¹,

Patange²,

Jegadeeshwaran³

et al. 2022

Structural Durability &Amp; Health Monitoring

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The regulation of tyre pressure is treated as a significant aspect of 'tyre maintenance' in the domain of autotronics. The manual supervision of a tyre pressure is typically an ignored task by most of the users. The existing instrumental scheme incorporates stand-alone monitoring with pressure and/or temperature sensors and requires regular manual conduct. Hence these schemes turn to be incompatible for on-board supervision and automated prediction of tyre condition. In this perspective, the Machine Learning (ML) approach acts appropriate as it exhibits comparison of specific performance in the past with present, intended for predicting the same in near future. The current investigation experimentally assesses the suitability of ML scheme for vibration based on-board supervision of tyre pressure of two wheeled vehicle. In order to examine the vibration response of a wheel hub, the in-house design & development of DAQ (Data Acquisition System) is described. Micro Electro-Mechanical Scheme (MEMS) built accelerometer is incorporated with open source hardware and software to collect and store the data. This framework is easy to develop, monitor and can be retrofitted in two wheeled vehicle. For various pressure conditions, the change in response of wheel hub vibration with respect to time is collected. The statistical parameters describing these vibration signals are determined and the decision tree is applied to select distinguishing parameters between extracted parameters. The classification of different conditions of tyre pressure is carried out using ML classifiers.

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Section: In-house Design and Development Of Daqmentioning

confidence: 99%

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Pardeshi¹,

Patange²,

Jegadeeshwaran³

et al. 2022

Structural Durability &Amp; Health Monitoring

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“…It is generally necessary to disarrange the order of original sample data to test it. The trained model was used to classify datasets [ 30 ].…”

Section: Prediction Model Of Milling Sound Signal Based On Bp Neural ...mentioning

confidence: 99%

Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites

et al. 2022

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The accuracy of the acoustic signal prediction model for wood–plastic composites milling has an important influence on the condition monitoring of the cutting process and the improvement of the machining environment. To establish a high-precision prediction model of sound signal in the high-speed milling of wood–plastic composites, high-speed milling experiments on self-developed wood–plastic composites were carried out with cemented carbide tools. A mathematical model of the relationship of the four milling parameters, including axial cutting depth, radial cutting depth, feed rate and cutting speed, and the sound signal of wood–plastic composites milling, was established by using the full-factor test method. The experimental data obtained by the orthogonal test method were used as the test samples in the mathematical model. Test results show that the prediction accuracy of the mathematical model of the sound signal in the milling of wood–plastic composites exceeds 95.4%. To further improve the prediction accuracy of the sound signal in the milling of wood–plastic composites, a prediction model was established using back propagation (BP) neural network. Then, the particle swarm optimization (PSO) algorithm was used to optimize the BP neural network, obtaining the PSO–BP neural network prediction model. The test results show that the prediction accuracy of the PSO–BP prediction model for the sound signal in the high-speed milling of wood–plastic composites exceeds 97.5%. The PSO–BP model has a better global approximation ability and higher prediction accuracy than the BP model. The research results can provide a reference basis for sound signal prediction in the high-speed milling of wood–plastic composites.

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“…The vibration signal of each state was divided into 75 samples for feature extraction, and the length of each sample was 2048. Then, 25 samples of each state were randomly selected to train the ELM classifier, and the remaining samples were used for testing [ 33 ]. Therefore, the total number of training and test sets was 125 and 250, respectively.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings

Guo

Zou³

et al. 2022

Sensors

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When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals. However, TPE can only characterize the complexity of the vibration signals at a single scale. Therefore, a multiscale transition permutation entropy (MTPE) technique has been proposed. However, the original multiscale method still has some inherent defects in the coarse-grained process, such as considerably shortening the length of time series at large scale, which leads to a low entropy evaluation accuracy. In order to solve these problems, a composite multiscale transition permutation entropy (CMTPE) method was proposed in order to improve the incomplete coarse-grained analysis of MTPE by avoiding the loss of some key information in the original fault signals, and to improve the performance of feature extraction, robustness to noise, and accuracy of entropy estimation. A fault diagnosis strategy based on CMTPE and an extreme learning machine (ELM) was proposed. Both simulation and experimental signals verified the advantages of the proposed CMTPE method. The results show that, compared with other comparison strategies, this strategy has better robustness, and can carry out feature recognition and bearing fault diagnosis more accurately and with improved stability.

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Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling

Cited by 26 publications

References 41 publications

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Tyre Pressure Supervision of Two Wheeler Using Machine Learning

Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites

Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings

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