A virtual sensing based augmented particle filter for tool condition prognosis

Wang, Jinjiang; Zheng, Yinghao; Wang, Peng; Gao, Robert X.

doi:10.1016/j.jmapro.2017.04.014

Cited by 37 publications

(10 citation statements)

References 23 publications

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“…When partial observations are made and random perturbations are present in the data, methods such as Kalman filters and particle filters [ 5 ] can be relied on for the estimation of the internal states of dynamical systems. These methods compute the unknown quantities through posterior distributions obtained from Bayesian inference models [ 6 , 7 ]. Although particle filters are not subject to the constraints of non-Gaussianity of perturbations and linearity of the dynamic systems that affect Kalman ones, they have some disadvantages as well, related to possible resampling biases and to the coarseness of the definition of the likelihood distribution, which may be unable to capture all relevant real-world characteristics.…”

Section: Related Workmentioning

confidence: 99%

Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments

Brunello

Urgolo

Pittino

et al. 2021

Sensors

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Real-time estimation of temperatures in indoor environments is critical for several reasons, including the upkeep of comfort levels, the fulfillment of legal requirements, and energy efficiency. Unfortunately, setting an adequate number of sensors at the desired locations to ensure a uniform monitoring of the temperature in a given premise may be troublesome. Virtual sensing is a set of techniques to replace a subset of physical sensors by virtual ones, allowing the monitoring of unreachable locations, reducing the sensors deployment costs, and providing a fallback solution for sensor failures. In this paper, we deal with temperature monitoring in an open space office, where a set of physical sensors is deployed at uneven locations. Our main goal is to develop a black-box virtual sensing framework, completely independent of the physical characteristics of the considered scenario, that, in principle, can be adapted to any indoor environment. We first perform a systematic analysis of various distance metrics that can be used to determine the best sensors on which to base temperature monitoring. Then, following a genetic programming approach, we design a novel metric that combines and summarizes information brought by the considered distance metrics, outperforming their effectiveness. Thereafter, we propose a general and automatic approach to the problem of determining the best subset of sensors that are worth keeping in a given room. Leveraging the selected sensors, we then conduct a comprehensive assessment of different strategies for the prediction of temperatures observed by physical sensors based on other sensors’ data, also evaluating the reliability of the generated outputs. The results show that, at least in the given scenario, the proposed black-box approach is capable of automatically selecting a subset of sensors and of deriving a virtual sensing model for an accurate and efficient monitoring of the environment.

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Section: Related Workmentioning

confidence: 99%

Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments

Brunello

Urgolo

Pittino

et al. 2021

Sensors

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“…Typical methods developed for estimating tool wear have relied upon either empirical models or the information collected from various types of sensors. Thus, these tool wear estimation methods can be divided into two broad categories, including physics-based and data-driven methods [11,12]. The empirical models developed in physics-based estimation methods seek to describe the physics of a machining system, and then determine the values of model parameters through extensive experiments.…”

Section: Introductionmentioning

confidence: 99%

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Lei

Zhu

Zhou

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning machine (ELM) method enhanced by a hybrid genetic algorithm and particle swarm optimization (GAPSO) approach for conducting tool wear estimation based on workpiece vibration signals. Here, a few feature parameters in the time, frequency, and time-frequency (Ensemble empirical mode decomposition, EEMD) domains of the workpiece vibration signals are extracted as the input of the ELM model. Then, the initialized weights and thresholds of the ELM model are optimized based on the GAPSO approach with training dataset. Finally, tool wear is estimated using the optimized ELM model with testing dataset. The effectiveness of the proposed method is verified by its application to vibration signals collected from two milling tool wear experiments (an open-access benchmark dataset and a milling tool wear experiment) by comparison to the ELM, GA-ELM, and PSO-ELM methods. The results indicate that the estimation accuracy and optimization efficiency of the proposed method outperforms that of other three methods.

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“…Tool wear monitoring or estimating is usually divided into direct monitoring and indirect monitoring [6]. Direct sensing techniques of a tool wear by using microscope or charged-couple-device (CCD) camera is a traditional visionbased tool wear measurement method [7]. But this method has to stop machine and remove the tool from holder.…”

Section: Introductionmentioning

confidence: 99%

An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece

Yu¹,

Wang

Zhang³

et al. 2021

Mathematical Problems in Engineering

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Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.

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A virtual sensing based augmented particle filter for tool condition prognosis

Cited by 37 publications

References 23 publications

Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments

Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

An Improved Tool Wear Monitoring Method Using Local Image and Fractal Dimension of Workpiece

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