Remote sensing based on time variance control in configurable area partitioning

Abstract. In this paper a sensor data fusion approach for characteristics field monitoring, based on time variance control model, is proposed. Distributed sensing and remote processing are the basic features of the employed architecture. In fact, in order to obtain meaningful information about the temporal and spatial variations, which characterize the field levels of some characteristics (electromagnetic, air pollution, seismic, etc), a distributed network of wireless and mobile smart-sensors has been designed.Starting from the partitioned configuration of a monitored geographic areas, this model allows to take into account the different levels of degradation over time in the sensors' performances associated with the different geographic partitions, progressively increasing the severity of the control. To this end, through the introduction of a reliability curve, a revised traditional control chart for variables is proposed.The proposed approach, further constituting an element of the scientific debate, aims to be a useful operational tool for professionals and managers employed in the environment control

Modelling survival : exposure pattern, species sensitivity and uncertainty

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans

Milling cutter condition reliability prediction based on state space model

Reliability analysis based on equipment's performance degradation characteristics is one of important research areas for reliability engineering. Many researcher work on multi-sample analysis, but it is limited for single equipment or small sample reliability prediction. Therefore, the method of reliability prediction based on state space model (SSM) is investigated in this research for small sample analysis. Firstly, signals about machine working conditions are collected based on-line monitoring technology. Secondly, wavelet packet energy parameters are determined based on the monitored signals. Frequency band energy is regarded as characteristic parameter. Then, the degradation characteristics of signal to noise ratio is improved by moving average filtering processing. In the end, SSM is established to predict degradation characteristics of probability density distribution, and the degree of reliability is determined. Milling cutter is used to demonstrate the rationality and effectiveness of this method. It can be concluded that this method is effective for milling cutter reliability estimation based on the data analysis. It also contributes to machine condition remaining useful life prediction

A Novel Hybrid Method of Parameters Tuning in Support Vector Regression for Reliability Prediction: Particle Swarm Optimization Combined With Analytical Selection

Support vector regression (SVR) is a widely used technique for reliability prediction. The key issue for high prediction accuracy is the selection of SVR parameters, which is essentially an optimization problem. As one of the most effective evolutionary optimization methods, particle swarm optimization (PSO) has been successfully applied to tune SVR parameters and is shown to perform well. However, the inherent drawbacks of PSO, including slow convergence and local optima, have hindered its further application in practical reliability prediction problems. To overcome these drawbacks, many improvement strategies are being developed on the mechanisms of PSO, whereas there is little research exploring a priori information about historical data to improve the PSO performance in the SVR parameter selection task. In this paper, a novel method controlling the inertial weight of PSO is proposed to accelerate its convergence and guide the evolution out of local optima, by utilizing the analytical selection (AS) method based on a priori knowledge about SVR parameters. Experimental results show that the proposed ASPSO method is almost as accurate as the traditional PSO and outperforms it in convergence speed and ability in tuning SVR parameters. Therefore, the proposed ASPSO-SVR shows promising results for practical reliability prediction tasks

Residual Life Distributions from Component Degradation Signals: A Bayesian Approach

Received and accepted Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can the

Condition-based prediction of time-dependent reliability in composites

This paper presents a reliability-based prediction methodology to obtain the remaining useful life of composite materials subjected to fatigue degradation. Degradation phenomena such as stiffness reduction and increase in matrix micro-cracks density are sequentially estimated through a Bayesian filtering framework that incorporates information from both multi-scale damage models and damage measurements, that are sequentially collected along the process. A set of damage states are further propagated forward in time by simulating the damage progression using the models in the absence of new damage measurements to estimate the time-dependent reliability of the composite material. As a key contribution, the estimation of the remaining useful life is obtained as a probability from the prediction of the time-dependent reliability, whose validity is formally proven using the axioms of Probability Logic. A case study is presented using multi-scale fatigue damage data from a cross-ply carbon-epoxy laminate

Studi Sistem Monitoring Prediksi Keandalan Real-Time Pada Sistem Pengendalian Kecepatan Generator Turbin Angin Dengan Kesalahan Sensor

Peningkatan jumlah penduduk seiring dengan peningkatan kebutuhan energi listrik yang ada di Indonesia. Salah satu cara yang sedang digalakkan untuk pemenuhan kebutuhan listrik melalui energi alternatif adalah dengan menggunakan turbin angin. Sementara sistem turbin angin sedang digunakan, keandalannya akan menurun secara bertahap. Penelitian ini bertujuan untuk menentukan parameter desain yang mempengaruhi prediksi keandalan real-time pada sistem pengendalian kecepatan generator turbin angin. Tiga langkah yang perlu dilakukan yaitu perancangan observer, perancangan algoritma prediksi kesalahan sensor dan perancangan algoritma prediksi keandalan. Perancangan observer digunakan untuk mengestimasi kesalahan sensor dari variabel yang terukur, kemudian hasil estimasi digunakan untuk menghitung prediksi kesalahan sensor melalui algoritma exponential smoothing. Hasil dari prediksi kesalahan sensor ini secara langsung digunakan untuk prediksi keandalan real-time. Waktu kegagalan riil ketika kecepatan generator lebih besar dari 1.2 pu dan kecepatan generator dibawah 0.5 pu menandakan keandalan mulai turun. Variasi diberikan terhadap time interval sebesar 1 detik, 5 detik, 10 detik dan 50 detik, sedangkan setiap time interval diberikan variasi jumlah prediksi sebesar 1, 5 dan 10. Hasil simulasi menunjukkan bahwa prediksi keandalan dengan time interval sebesar 1 detik lebih tepat jika dibandingkan variasi time interval sebesar 5 detik, 10 detik, dan 50 detik. Jumlah prediksi mempengaruhi ketelitian prediksi keandalan real-time yang dihasilkan, semakin besar jumlah prediksi maka semakin teliti. Dengan time interval 1 detik, jumlah prediksi sebesar 10 menghasilkan prediksi keandalan real-time lebih tepat dibandingkan jumlah prediksi sebesar 1 dan 5. =============================================================================================== Indonesia is known as the largest archipelagic country in the world. It has a significant growth in its population every year. As the result, the energy demand in Indonesia continues to develop. One of the method of helping meet energy needs is by introducing wind turbine as alternative energy source. However, the reliability on the turbine system will be decreasing steadly if it is being continually exploited. This study aims to determine the design parameters that is affecting the real-time reliability prediction for wind turbine generator speed control systems. There are three steps that need to be done, namely observer design, sensor fault prediction algorithm design, and reliability prediction algorithm design. Observer design was used to estimate sensor fault from the measured variable and the estimated result was used to calculate sensor fault prediction using exponential smoothing algorithm. The calculated result will then be directly exploited to predict real-time reliability. We obtained that the failure time correspond with a decrease in the reliability when the generator speed is greater than 1.2 pu and below 0.5 pu. Time interval was varied by 1s, 5s, 10s, and 50s. In each of the time interval, the variation number of prediction given was of 1, 5, and 10. Simulation result has shown that reliability prediction with time interval 1s was more precise compared to time interval variation of 5s, 10s, and 50s. The number of prediction affects real-time reliability prediction fidelity resulted. With the time interval of 1s, the number of prediction of 10 results in accurate real-time reliability prediction compared to number of prediction of 1 and 5

Nonlinear Stochastic Dynamic Systems Approach for Personalized Prognostics of Cardiorespiratory Disorders

This research investigates an approach rooted in nonlinear stochastic dynamic systems principles for personalized prognostics of cardiorespiratory disorders in the emerging point-of-care (POC) treatment contexts. Such an approach necessitates new methods for (a) quantitative and personalized modeling of underlying cardiovascular system dynamics to serve as a virtual instrument to derive surrogate (hemodynamic) signals, (b) high-specificity diagnostics to identify and localize disorders, (c) real-time prediction to provide forecasts of impending disorder episodes, and (d) personalized prognosis of the short-term variations of the risk, necessary for effective treatment decisions, based on estimating the distribution of the times remaining till the onset of an anomaly episode. The specific contributions of the dissertation work are as follows: 1. Quantitative modeling for real-time synthesis of hemodynamic signals. Features extracted from ECG signals were used to construct atrioventricular excitation inputs to a nonlinear deterministic lumped parameter model of cardiovascular system dynamics. The model-derived hemodynamic signals, personalized to an individual's physiological and anatomical conditions, would lead to cost-effective virtual medical instruments necessary for personalized POC prognostics. 2. Random graph representation of the complex cardiac dynamics for disorder diagnostics. The quantifiers of a random walk on a network reconstructed from vectorcardiogram (VCG) were investigated for the detection and localization of cardiovascular disorders. Extensive tests with signals from PTB database of PhysioNet databank suggest that locations of myocardial infarction can be determined accurately (sensitivity of ~88% and specificity of ~92%) from tracking certain consistently estimated invariants of this random walk representation. 3. Nonparametric prediction modeling of disorder episodes. A Dirichlet process based mixture Gaussian process was utilized to track and forecast the evolution of the complex nonlinear and nonstationary cardiorespiratory dynamics underlying of the measured signal features and health states. Extensive sleep tests suggest that the method can predict an impending sleep apnea episode to accuracies (R^2) of 83% and 77% for 1 step and 3 step-ahead predictions, respectively.4. Color-coded random graph representation of the state space for personalized prognostic modeling. The prognostic model used the stochastic evolution of the transition pathways from a normal state to an anomalous state in the color-coded state space network to estimate the distribution of the remaining useful life. The prognostic model was validated using the data from ECG Apnea Database (Physionet.org). The model can predict the estimated time till a disorder (apnea episode) onset to within 15% of the observed times 1-45 min ahead of their inception.Industrial Engineering & Managemen