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This paper proposes an incipient chatter detection method to meet high dynamic applications' time and reliability constraints, such as high-speed milling involving heavy noise. The herein introduced method relies on a multiple sampling per revolution (MSPR) technique, coupled with two data preprocessing techniques, a modified adaptive cumulative chatter indicator, and a two-risk levels-based threshold. The MSPR technique enables collecting information-rich enough data to characterize the chatter dynamics thanks to a significant amount of data collected in each revolution. Therefore, the MSPR technique allows for acquiring the data using a short-time window, thus reducing the detection delay. Two data preprocessing techniques, i.e., Z-score normalization and mean-centered, are implemented for data integration and chatter information consolidation. The modified adaptive cumulative chatter indicator has three advantages: (a) it accumulates the information on the chatter feature and highlights the appearance of an incipient chatter; (b) it adapts to the variation of the environmental disturbance noises, resulting in enhanced detection reliability; (c) it is faster than the adaptive cumulative log-likelihood ratio (ACLLR) for decision-making statistically. The two-risk levels-based threshold overcomes the limitations of a unique threshold, and allows simultaneously assessing the two risk levels, thus improving detection reliability. We successfully applied the proposed method to detect incipient chatter in a digital high-speed milling process and assessed its effectiveness by comparing it with several existing chatter detection methods.

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This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO2]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors' sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO2] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration.

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Real-time detection of early chatter is a vital strategy to improve machining quality and material removal rate in the high-speed milling processes. This paper proposes a maximum entropy (MaxEnt) feature-based reliability model method for real-time detection of early chatter based on multiple sampling per revolution (MSPR) technique and second-order reliability method (SORM). To enhance the detection reliability, the MSPR is used to acquire multiple sets of once-per-revolution sampled data (i.e., MSPR data) and to overcome the shortcoming of the once-per-revolution sampling. The proposed MaxEnt feature-based reliability model method solves the issue of the real-time detection of early chatter while ensuring its reliability. The failure hazard function (FHF) is estimated as a chatter indicator by using the SORM with the MaxEnt feature. The proposed method consists of five steps. First, set the prior parameters. Then collect data by using the MSPR technique. Next, calculate a set of the standard deviation of the data collected as a chatter feature and estimate the chatter indicator FHF by applying the SORM with the MaxEnt feature. Finally, implement the real-time detection of early chatter based on the estimated chatter indicator FHF and the threshold FHF0. The proposed method is applied to the high-speed milling process. Two examples prove that the proposed method can detect two kinds of early chatter: the early-stage of a severe chatter and the slightly intolerable chatter.

RESUMEN

Recently, the emergence of low-cost sensors have allowed electronic noses to be considered for densifying the actual air pollution monitoring networks in urban areas. Electronic noses are affected by changes in environmental conditions and sensor drifts over time. Therefore, they need to be calibrated periodically and also individually because the characteristics of identical sensors are slightly different. For these reasons, the calibration process has become very expensive and time consuming. To cope with these drawbacks, calibration transfer between systems constitutes a satisfactory alternative. Among them, direct standardization shows good efficiency for calibration transfer. In this paper, we propose to improve this method by using kernel SPXY (sample set partitioning based on joint x-y distances) for data selection and support vector machine regression to match between electronic noses. The calibration transfer approach introduced in this paper was tested using two identical electronic noses dedicated to monitoring nitrogen dioxide. Experimental results show that our method gave the highest efficiency compared to classical direct standardization.