Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications

At present, decision making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, Convolutional Neural Network (CNN) can learn effective representations of health states from industrial data. Many developed CNN-based schemes, such as advanced CNNs that introduce residual learning and multi-scale learning, have shown good performance in health state recognition tasks under the assumption that all the classes are known. However, these schemes have no ability to deal with new abnormal samples that belong to state classes not part of the training set. In this paper, a collective decision framework for different CNNs is proposed. It is based on a One-vs-Rest network (OVRN) to simultaneously achieve classification of known and unknown health states. OVRN learn state-specific discriminative features and enhance the ability to reject new abnormal samples incorporated to different CNNs. According to the validation results on the public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based decision schemes incorporating OVRN have outstanding recognition ability for samples of unknown heath states, while maintaining satisfactory accuracy on known states. The results show that the new DL framework outperforms conventional CNNs, and the one based on residual and multi-scale learning has the best overall performance

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries

Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation

Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance. However, like any sensory data, urban traffic data are prone to imperfections leading to missing measurements. In this paper, we focus on inter-region traffic data completion. We model the inter-region traffic as a spatiotemporal tensor that suffers from missing measurements. To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic. To derive the urban characteristics, we divide the area of study into regions. Then, for each region, we compute urban feature vectors inspired from biodiversity which are used to compute the urban similarity matrix. To mine the temporal aspect, we first conduct an entropy analysis to determine the most regular time-series. Then, we conduct a joint Fourier and correlation analysis to compute its periodicity and construct the temporal matrix. Both urban and temporal matrices are fed into a modified CP-completion objective function. To solve this objective, we propose an alternating least square approach that operates on the vectorized version of the inputs. We conduct comprehensive comparative study with two evaluation scenarios. In the first one, we simulate random missing values. In the second scenario, we simulate missing values at a given area and time duration. Our results demonstrate that our approach provides effective recovering performance reaching 26% improvement compared to state-of-art CP approaches and 35% compared to state-of-art generative model-based approaches

Process fault prediction and prognosis based on a hybrid technique

The present study introduces a novel hybrid methodology for fault detection and diagnosis (FDD) and fault prediction and prognosis (FPP). The hybrid methodology combines both data-driven and process knowledge driven techniques. The Hidden Markov Model (HMM) and the auxiliary codes detect and predict the abnormalities based on process history while the Bayesian Network (BN) diagnoses the root cause of the fault based on process knowledge. In the first step, the system performance is evaluated for fault detection and diagnosis and in the second step, prediction and prognosis are evaluated. In both cases, an HMM trained with Normal Operating Condition data is used to determine the log-likelihoods (LL) of each process history data string. It is then used to develop the Conditional Probability Tables of BN while the structure of BN is developed based on process knowledge. Abnormal behaviour of the system is identified through HMM. The time of detection of an abnormality, respective LL value, and the probabilities of being in the process condition at the time of detection are used to generate the likelihood evidence to BN. The updated BN is then used to diagnose the root cause by considering the respective changes of the probabilities. Performance of the new technique is validated with published data of Tennessee Eastman Process. Eight of the ten selected faults were successfully detected and diagnosed. The same set of faults were predicted and prognosed accurately at different levels of maximum added noise

Quantum back-action evasion and filtering in optomechanical systems

The measurement precision of optomechanical sensors reached sensitivity levels such that they have to be described by quantum theory. In quantum mechanics, every measurement will introduce a back-action on the measured system itself. For optomechanical force sensors, a trade-off between back-action and measurement precision exists through the interplay of quantum shot noise and quantum radiation pressure noise. Finding the optimal power to balance these effects leads to the standard quantum limit (SQL), which bounds the sensitivity of force sensing. To overcome the SQL and reach the fundamental bound of parameter estimation, the quantum Cramér-Rao bound, techniques called quantum smoothing and quantum back-action evasion are required. The first part of this thesis explores quantum smoothing in the context of optomechanical force sensing. Quantum smoothing combines the concepts of prediction and retrodiction to estimate the parameters of a system in the past. To illustrate the intricacies of these estimations in the quantum setting, two filters, the Kalman and Wiener filters, are introduced. Their prediction and retrodiction estimates are given for a simple optomechanical setup, and resulting differences are analyzed concerning the available quantum smoothing theories in the literature. In the second part of this thesis, a back-action evasion technique called coherent quantum-noise cancellation (CQNC) is explored. In CQNC, an effective negative-mass oscillator is coupled to an optomechanical sensor to create destructive interference of quantum radiation pressure noise. An all-optical realization of such an effective negative-mass oscillator is introduced, and a comprehensive study of its performance in a cascaded CQNC scheme is given. We determine ideal CQNC conditions, analyze non-ideal noise cancellation and provide a case study. Under feasible parameters, the case study shows a possible reduction of radiation pressure noise of 20% and that the effective negative-mass oscillator as the first subsystem in the cascade is the preferable order.Die Messgenauigkeit optomechanischer Sensoren hat eine Sensitvität erreicht, sodass sie im Rahmen der Quantentheorie beschrieben werden müssen. Quantenmechanik besagt, dass jede Messung eine Rückkopplung auf das vermessene System induziert. Bei optomechanischen Kraftsensoren is ein Kompromiss zwischen Rückkopplung und Messgenauigkeit durch die Verzahnung von Schrotrauschen und Strahlungsdruckrauschen begründet. Die Verwendung der optimalen Leistung, derart dass diese beiden Prozesse in Waage liegen, führt zum Standardquantenlimit (SQL). Hierdurch wird die Messgenauigkeit begrenzt. Um das SQL zu überwinden und die fundamentale Grenze der Parameterschätzung zu erreichen, welche durch Quanten-Cramér-Rao-Ungleichung bestimmt ist, werden die Methoden der Quantenglättung und Rückkopplungsumgehung benötigt. Im ersten Teil dieser Arbeit wird das Gebiet der Quantenglättung im Kontext von optomechanischer Kraftmessung untersucht. Die Quantenglättung kombiniert die Methoden der Vorhersage und Retrodiktion, um Abschätzungen an die Parameter eines Quantensystems zu tätigen, welche in der Vergangenheit liegen. Um die Feinheiten dieser Abschätzungen für Quantensysteme zu demonstrieren, werden zwei Filter, der Kalman- und der Wiener-Filter eingeführt. An einem einfachen optomechanischen System, werden deren Ergebnisse für die Vorhersage und Retrodiktion berechnet. Mögliche Diskrepanzen werden im Kontext der verfügbaren Theorien der Quantenglättung beleuchtet. Im zweiten Teil dieser Dissertation wird eine Rückkopplungsumgehungsmethode, die kohärente Quantenrauschunterdrückung (coherent quantum-noise cancellation, CQNC) untersucht. Bei CQNC wird ein Oszillator mit effektiver negativer Masse an einen optomechanischen Sensor gekoppelt, um destruktiv mit dem Strahlungsdruckrauschen zu interferieren. Eine mögliche optische Realisierung eines solchen negativen Masse Oszillators wird vorgestellt und mit einem optomechanischem Kraftsensor kaskadiert. Dieser Aufbau wird hinsichtlich seiner Rauschünterdrückungfähigkeit untersucht. Diesbezüglich ermitteln wir die Bedingungen für eine vollständige Abwendung von Strahlungsdruckrauschen und analysieren den Einfluss von möglichen Abweichungen von diesen Bedingungen auf die Rauschünterdrückung. Zuletzt präsentieren wir eine Fallstudie eines möglichen experimentellen Aufbaus. Die Fallstudie zeigt eine mögliche Strahlungsdrückreduzierung von 20% und dass der Oszillator mit effektiver negativer Masse als erstes System in der Kaskade zu bervorzugen ist

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods