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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.

Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors.

International audience; 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.

International audience; In this paper, we propose a method based on piecewise linear approximation (PLA) with segmented projection error (SPE) to reveal the underlying correlation structures and approximate multivariate time series. Based on this method we elaborate an indicator called vector of segmented projection error (VSPE) to characterize the bearings degradation using standard time domain standard features extracted from the vibration signal. The indicator VSPE monitors the evolution of the entire degradation process efficiently and provides real-time information on bearings degradation. We show the effectiveness of this new indicator using benchmark data. The results reveal the sensitive and monotonic character of the indicator VSPE according to the bearings degradation, which is promising for monitoring bearings on their lifespan.

International audience; Online incipient chatter detection is a crucial task to improve surface quality, reduce dimensional errors, and protect the cutting tool and machine tool in the high-speed milling process. Since variable background noise in measured signal impacts the accuracy of the incipient chatter detection, it is necessary to develop an appropriate and efficient threshold to detect incipient chatter reliably. This paper proposes an online incipient chatter detection method for high-speed milling process based on once-per-revolution sampling and dynamic threshold variant. First, the once-per-revolution sampling technique is used to reduce the data size and acquire independent data of machining parameters. Next, an incipient chatter indicator MaxEnt, i.e., maximum entropy, is extracted from the once-per-revolution sampled data by using the MaxEnt principle. Finally, a dynamic threshold variant and sequential probability ratio test (SPRT) are used for incipient chatter detection. To validate the effectiveness of the proposed method, a simulated chatter signal, including seven components, is employed. The validated results show that the proposed method could detect the incipient chatter effectively in the variable noise environment.

International audience; In high-speed milling process, the early detection of chatter contains two aspects, one is the fast detection of the initial symptom of the chatter, the other is to recognize the slight chatter embedded in the noisy signal. This paper presents an online cumulative chatter detection method for high-speed milling process based on once-per-revolution sampling, maximum entropy (MaxEnt) principle and sequential probability ratio test (SPRT). The method allows for coping with these two aspects of early chatter detection. This method has less computational complexity and is independent of the cutting conditions. The procedure consists of four steps. First, the prior knowledge of early chatter is determined. Secondly, once-per-revolution sampling data is sampled from the vibration signal. Thirdly, the MaxEnt principle is used to estimate the MaxEnt of the once-per-revolution sampling data as a chatter indicator. Finally, the SPRT cumulates the information of the estimated MaxEnt, and then detect the early chatter by using the prior knowledge. The proposed strategy is applied to a high-speed milling process, and two simulation experiments allowed to assess the effectiveness of the early chatter detection method.

International audience; Condition-based maintenance of rotating mechanics requests efficient bearings degradation monitoring. The accuracy of bearings degradation measure depends largely on degradation indicators. This research aims to extract an indicator which can efficiently characterize the degradation of bearings. To that extent, in this paper we propose a method based on the segmented discarded Hotelling T square (SDHT 2 ) with a piecewise linear representation (PLR) approach. First, we used time domain common features extracted from the bearing vibration signal to roughly describe the bearing degradation. Then, several characteristic values are used to represent the whole historical degradation process by processing the vibration signal based time domain common features through the PLR approach. The SDHT 2 values are used as the characteristic values. The degradation indicator, named (VSDHT 2 ), is a vector where each entry is the discarded Hotelling T square value of a segment. Naturally, the new indicator VSDHT 2 describes the whole degradation process history and also carries the real-time information of bearings degradation. For illustration, a benchmark data set is used in this paper. The results show that the new indicator VSDHT 2 is sensitive and monotonic during the bearings whole lifecycle, which is promising to monitor bearings degradation.

This paper focuses on an approach to develop a robust, reliable, maintainable and proactive process of data collection through relevant indicators. Mainly, it aims to model and optimize the effectiveness of the RAMS data collection process (DCP), under cost constraints, while taking into account the its operating conditions and human factors. It also deals with the data collection system maintenance, data collection process and database maintenance over time. The authors have proposed an approach to model and optimize the RAMS DCP based on data relevance assessment, indicators of complexity, the usefulness and the quantity of information collected using the DCP and database.

Hydrothermal liquefaction of wet biomass such as algae is a promising thermochemical process for the production of bio-oil. Bio-oil aqueous phase generated during liquefaction process is rich in complex organics and can be utilized for biogas production following its pre-treatment with granular activated carbon. In our study, use of 30% activated carbon resulted in higher chemical oxygen demand (COD) reduction (53 ± 0.3%) from aqueous phase. Higher CH4 production (84 ± 12 mL/g COD) was also observed in 30% carbon-treated aqueous phase fed cultures, whereas only 32 ± 6 mL CH4/g COD was observed in control (non-carbon treated) cultures. The results from this study indicate that almost 67 ± 0.3% initial COD of aqueous phase can be reduced using a combination of both carbon treatment and biogas production. This study shows that aqueous phase can be utilized for CH4 production.

International audience; The authors propose a methodology to assess the effectiveness of a maintenance strategy on the availability of a serial-parallel multi-physic system, using Monte Carlo simulation embedded in a Petri net model. The systems are composed of heterogenous components that are characterized by specific degradations and failure mechanisms. Building an effective maintenance strategy to improve the availability of such a system requires to monitoring the degradation of each component. We assume that each component is subject to stochastic degradations. Also, we consider that each component might have three health status, according to degradation thresholds, function of the component reliability: “healthy”, “degraded” and “failed”. The health condition of the overall system relies on the health status of each component. A model for tracking the status of each component has been worked out using a colored stochastic Petri net (CSPN). Each health status is modeled by a place within the CSPN model, where each component is modeled by a colored token. The degradation of each component of the system is evaluated based on the Monte Carlo simulation technique. Transition firing regarding a given color model the evolution of the associated component from a health condition to another due to the degradation mechanism or to a maintenance action aimed to restore partially or totally its performance. However, the degradation of each component does not have the same influence on the performance of the overall system. Operational performance indicators are introduced to quantify the influence of each component on the performance of the entire system. Furthermore, maintenance actions are defined taking into account the degradation level of each component, the influence that each component has on the performance of the system and the available repairman. The effectiveness of the maintenance strategy on the system availability is evaluated through simulation.

Purpose – The purpose of this paper is to introduce a method for modelling the multi-state repairable systems subject to stochastic degradation processes by using the coloured stochastic Petri nets (CSPN). The method is a compact and flexible Petri nets model for multi-state repairable systems and offers an alternative to the combinatory of Markov graphs. Design/methodology/approach – The method is grounded on specific theorems used to design an algorithm for systematic construction of multi-state repairable systems models, whatever is their size. Findings – Stop and constraint functions were derived from these theorems and allow to considering k-out-of-n structure systems and to identifying the minimal cut sets, useful to monitoring the states evolution of the system. Research limitations/implications – The properties of this model will be studied, and new investigations will help to demonstrate the feasibility of the approach in real world, and more complex structure will be considered. Practical implications – The simulation models based on CSPN can be used as a tool by maintenance decision makers, for prediction of the effectiveness of maintenance strategies. Originality/value – The proposed approach and model provide an efficient tool for advanced investigations on the development and implementation of maintenance policies and strategies in real life.

International audience; A major goal of this paper is to consider a new paradigm of monitoring and predictive maintenance decision support of distributed hybrid renewable energy power units, driven by data, economic and ecologic requirements. A renewable energy power unit is a multi-component multistate complex system. In this paper, colored stochastic Petri nets are suggested to model, evaluate and forecast the health conditions of components, and to drive and update the data collection process through the unit lifetime. The approach allows monitoring the health conditions of the unit and predicting the reliability of its components, on the base of a non-parametric model.

The progress of nanoscience and nanotechnology can be realized only through continued advances and utilization of instruments and techniques for characterizing material properties and manipulating material and device at nanoscale. The UHV LT Multi-Probe SPM system with high resolution SEM has been developed to meet such challenges. This integrated instrument bridges dimensions from the centimeter to atomic scale, and provides an unprecedented platform for local, non-destructive transport measurements and for building, manipulating and function-testing complex nanoelectronics and nanoscale machineries. It also enables combining many different techniques for characterizing sample conductance, topography, chemical, optical or magnetic properties with complementary information at the same position or on the same nanodevice. Design and development of such complex systems pose many issues and challenges. This chapter will discuss some of the issues faced, solutions reached and advances made. Examples include: (1) Disturbance by magnetic material and magnetic field of SEM imaging and coordination of SEM/SPM position, and their influence on and disturbance of SAM spectra and SAM mapping. The design and use of non-magnetic motors for multi-probe modules will be presented and discussed. (2) Tip holder and sample holder design for easy handling, better mechanical stability over the temperature range and better thermal contact and the versatility of the sample holder with multiple contacts. (3) Use of optical fiber as one of the probe modules, and positioning of the fiber probe. CL spectra and CL mapping results will be presented.

International audience; This paper presents an approach of data collection for equipment's health condition monitoring on its lifespan. A methodology is proposed for implementing and evaluating the data collection system. Then a credible approach and a data stream model are proposed for managing the equipment's lifecycle and health. The model serves also to evaluate the equipment's performances and remaining useful life. In addition, it helps keeping the quality of the data stream. Lastly, a methodology for robust evaluation of equipment's performances, in presence of rror propagation risks is proposed.

We describe the development and the capabilities of an advanced system for nanoscale electrical transport studies. This system consists of a low temperature four-probe scanning tunneling microscope (STM) and a high-resolution scanning electron microscope coupled to a molecular-beam epitaxy sample preparation chamber. The four STM probes can be manipulated independently with subnanometer precision, enabling atomic resolution STM imaging and four-point electrical transport study of surface electronic systems and nanostructured materials at temperatures down to 10 K. Additionally, an integrated energy analyzer allows for scanning Auger microscopy to probe chemical species of nanostructures. Some testing results are presented.

The wide variety of nanoscale structures and devices demands novel tools for handling, assembly, and fabrication at nanoscopic positioning precision. The manipulation tools should allow for in situ characterization and testing of fundamental building blocks, such as nanotubes and nanowires, as they are built into functional devices. In this paper, a bottom-up technique for nanomanipulation and nanofabrication is reported by using a 4-probe scanning tunneling microscope (STM) combined with a scanning electron microscope (SEM). The applications of this technique are demonstrated in a variety of nanosystems, from manipulating individual atoms to bending, cutting, breaking carbon nanofibers, and constructing nanodevices for electrical characterizations. The combination of the wide field of view of SEM, the atomic position resolution of STM, and the flexibility of multiple scanning probes is expected to be a valuable tool for rapid prototyping in the nanoscience and nanotechnology.

A single-tube scanning tunneling microscope has been zeta-calibrated by using atomic steps of crystalline gold and was used for measuring the thickness of two biological samples, metal-coated as well as uncoated. The hexagonal surface layer of the bacterium Deinococcus radiodurans with an open network-type structure shows thickness values that are strongly influenced by the substrate and the preparation method. In contrast, the thickness of the purple membrane of Halobacterium halobium with its densely packed less-corrugated structure exhibits very little variation in thickness in coated preparations and the values obtained are in good agreement with x-ray data.

STM topographical images of C60 are reported. The images are consistent with a molecule approximately 9 Å in diameter possessing the now-famous soccer ball structure. With the molecule deposited on gold, its atomic structure is not resolved. On graphite the structure of the within the borders of the C60 molecule is dominated by that of the graphite forming a moiré-like pattern. Some evidence of atomic structure is seen in multilayers of C60 where some five- and six-membered rings are visible. These may, however, be features of fragments of the fullerene rather than whole molecules.

Scanning tunneling microscopy images of uncoated purple membrane are easily obtained at tunneling currents lower than 1 pA and tunneling voltages above ∼7 V. However, the images sometimes show positive, sometimes negative contrast. This effect appears to depend only on the shape of the tunneling tip. A simple model, assuming a constant voltage drop across the purple membrane, is proposed which can explain this observation.

Imaging of biological macromolecules by scanning tunneling microscopy is hampered by the poor electrical conductivity of these objects. We found, however, that by controlling the protein hydration a small but sufficient conductivity can be induced. At tunneling currents lower than 0.5 pA and tunneling voltages up to 10 V and at a humidity in the range between 30% and 45% it was possible to obtain clear images of the HPI layer, a protein which naturally forms a two-dimensional ordered array on a bacterial surface.