8,423 research outputs found
Multimode laser cooling and ultra-high sensitivity force sensing with nanowires
Photo-induced forces can be used to manipulate and cool the mechanical motion
of oscillators. When the oscillator is used as a force sensor, such as in
atomic force microscopy, active feedback is an enticing route to enhancing
measurement performance. Here, we show broadband multimode cooling of dB
down to a temperature of ~K in the stationary regime. Through the use
of periodic quiescence feedback cooling, we show improved signal-to-noise
ratios for the measurement of transient signals. We compare the performance of
real feedback to numerical post-processing of data and show that both methods
produce similar improvements to the signal-to-noise ratio of force
measurements. We achieved a room temperature force measurement sensitivity of
N with integration time of less than ms. The high
precision and fast force microscopy results presented will potentially benefit
applications in biosensing, molecular metrology, subsurface imaging and
accelerometry.Comment: 16 pages and 3 figures for the main text, 14 pages and 5 figures for
the supplementary informatio
Modern microwave methods in solid state inorganic materials chemistry: from fundamentals to manufacturing
No abstract available
A comparison study of distribution-free multivariate SPC methods for multimode data
The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to the underlying statistical distribution may not be appropriate, and conventional MSPC methods may deliver unacceptable performances. In addition, in many practical applications, the process switches from one operating mode to a different one, leading to a stream of multimode data. Various nonparametric approaches have been proposed for the design of multivariate control charts, but the monitoring of multimode processes remains a challenge for most of them. In this study, we investigate the use of distribution-free MSPC methods based on statistical learning tools. In this work, we compared the kernel distance-based control chart (K-chart) based on a one-class-classification variant of support vector machines and a fuzzy neural network method based on the adaptive resonance theory. The performances of the two methods were evaluated using both Monte Carlo simulations and real industrial data. The simulated scenarios include different types of out-of-control conditions to highlight the advantages and disadvantages of the two methods. Real data acquired during a roll grinding process provide a framework for the assessment of the practical applicability of these methods in multimode industrial applications
An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process
Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods
Anomaly detection and mode identification in multimode processes using the field Kalman filter
A process plant can have multiple modes of operation due to varying demand, availability of resources or the fundamental design of a process. Each of these modes is considered as normal operation. Anomalies in the process are characterised as deviations away from normal operation. Such anomalies can be indicative of developing faults which, if left unresolved, can lead to failures and unplanned downtime. The Field Kalman Filter (FKF) is a model-based approach, which is adopted in this paper for monitoring a multimode process. Previously, the FKF has been applied in process monitoring to differentiate normal operation from known faulty modes of operation. This paper extends the FKF so that it may detect occurrences of anomalies and differentiate them from the various normal modes of operation. A method is proposed for offline training an FKF monitoring model and on-line monitoring. The off-line part comprises training an FKF model based on Multivariate Autoregressive State-Space (MARSS) models fitted to historical process data. A monitoring indicator is also introduced. On-line monitoring, on the basis of the FKF for anomaly detection and mode identification, is demonstrated using a simulated multimode process. The performance of the proposed method is also demonstrated using data obtained from a pilot scale multiphase flow facility. The results show that the method can be applied successfully for anomaly detection and mode identification
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