Remaining lifetime of degrading systems continuously monitored by degrading sensors

We consider degrading engineering systems monitored by degrading sensors. Since accurate information is crucial for predicting system health condition and the subsequent decision-making, considering the effect of sensor degradation is highly important to determine the justified reliability characteristics of systems such as the remaining useful life (RUL). Although the concept of sensor degradation has been introduced previously in the literature, the remaining useful life estimation in this case or parameter estimation in the presence of sensor degradation has not been studied in detail. To fill the gap, this study aims to estimate the RUL of a system that is continuously monitored by a degrading sensor. In this work, to distinguish sensor degradation from that of the main system, an additional calibration sensor is used to accurately inspect the system health condition at certain points of time. Subsequently, maximum-a-posteriori estimation technique is employed to estimate the parameters for the system degradation process and maximum likelihood estimation is used to estimate the parameters of sensor degradation. A Kalman filter is then used to estimate the system and sensor states, followed by system RUL evaluation. A numerical example with simulated data is employed to illustrate the effectiveness of the proposed method. It is shown through the numerical study that neglecting sensor degradation can result in significant errors in RUL estimation, which can further impact the subsequent maintenance decisions

Condition-based maintenance at both scheduled and unscheduled opportunities

Motivated by original equipment manufacturer (OEM) service and maintenance practices we consider a single component subject to replacements at failure instances and two types of preventive maintenance opportunities: scheduled, which occur due to periodic system reviews of the equipment, and unscheduled, which occur due to failures of other components in the system. Modelling the state of the component appropriately and incorporating a realistic cost structure for corrective maintenance as well as condition-based maintenance (CBM), we derive the optimal CBM policy. In particular, we show that the optimal long-run average cost policy for the model at hand is a control-limit policy, where the control limit depends on the time until the next scheduled opportunity. Furthermore, we explicitly calculate the long-run average cost for any given control-limit time dependent policy and compare various policies numerically.Comment: published at proceedings of the 9th IMA International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR), 201

An Approach to Assess Solder Interconnect Degradation Using Digital Signal

Department of Human and Systems EngineeringDigital signals used in electronic systems require reliable data communication. It is necessary to monitor the system health continuously to prevent system failure in advance. Solder joints in electronic assemblies are one of the major failure sites under thermal, mechanical and chemical stress conditions during their operation. Solder joint degradation usually starts from the surface where high speed signals are concentrated due to the phenomenon referred to as the skin effect. Due to the skin effect, high speed signals are sensitive when detecting the early stages of solder joint degradation. The objective of the thesis is to assess solder joint degradation in a non-destructive way based on digital signal characterization. For accelerated life testing the stress conditions were designed in order to generate gradual degradation of solder joints. The signal generated by a digital signal transceiver was travelling through the solder joints to continuously monitor the signal integrity under the stress conditions. The signal properities were obtained by eye parameters and jitter, which represented the characteristics of the digital signal in terms of noise and timing error. The eye parameters and jitter exhibited significant increase after the exposure of the solder joints to the stress conditions. The test results indicated the deterioration of the signal integrity resulted from the solder joint degradation, and proved that high speed digital signals could serve as a non-destructive tool for sensing physical degradation. Since this approach is based on the digital signals used in electronic systems, it can be implemented without requiring additional sensing devices. Furthermore, this approach can serve as a proactive prognostic tool, which provides real-time health monitoring of electronic systems and triggers early warning for impending failure.ope

UK domestic air conditioning: a study of occupant use and energy efficiency

This paper presents the results of a study of air-conditioning usage in homes in the southeast of England. First part of the study consisted surveying 13 dwellings with air-conditioning for a series of 4 week periods during the summer of 2004. The second part involved testing energy efficiency of “single-split” and “portable” air-conditioning units under “in-use” conditions. Data on usage patterns and typical temperature profiles during operation was collected and is presented here. Temperatures at which users switched their units on were, on average, 24-25oC, while typical running times for a single operation were found to be around 5 hours during daytime and 7 hours at night in bedrooms. The study also indicated high occupant satisfaction rates with split-units. An unexpectedly high overall energy efficiency ratio (EER), of 5-10, was found for the single-split unit tested during the relatively mild autumn weather. However, a very poor EER, of less than 1, was found for the portable unit tested. Further work is needed to increase the reliability and statistical significance of the results

Modeling cloud resources using machine learning

Cloud computing is a new Internet infrastructure paradigm where management optimization has become a challenge to be solved, as all current management systems are human-driven or ad-hoc automatic systems that must be tuned manually by experts. Management of cloud resources require accurate information about all the elements involved (host machines, resources, offered services, and clients), and some of this information can only be obtained a posteriori. Here we present the cloud and part of its architecture as a new scenario where data mining and machine learning can be applied to discover information and improve its management thanks to modeling and prediction. As a novel case of study we show in this work the modeling of basic cloud resources using machine learning, predicting resource requirements from context information like amount of load and clients, and also predicting the quality of service from resource planning, in order to feed cloud schedulers. Further, this work is an important part of our ongoing research program, where accurate models and predictors are essential to optimize cloud management autonomic systems.Postprint (published version

Integrated performance prediction and quality control in manufacturing systems

textPredicting the condition of a degrading dynamic system is critical for implementing successful control and designing the optimal operation and maintenance strategies throughout the lifetime of the system. In many situations, especially in manufacturing, systems experience multiple degradation cycles, failures, and maintenance events throughout their lifetimes. In such cases, historical records of sensor readings observed during the lifecycle of a machine can yield vital information about degradation patterns of the monitored machine, which can be used to formulate dynamic models for predicting its future performance. Besides the ability to predict equipment failures, another major component of cost effective and high-throughput manufacturing is tight control of product quality. Quality control is assured by taking periodic measurements of the products at various stages of production. Nevertheless, quality measurements of the product require time and are often executed on costly measurement equipment, which increases the cost of manufacturing and slows down production. One possible way to remedy this situation is to utilize the inherent link between the manufacturing equipment condition, mirrored in the readings of sensors mounted on that machine, and the quality of products coming out of it. The concept of Virtual Metrology (VM) addresses the quality control problem by using data-driven models that relate the product quality to the equipment sensors, enabling continuous estimation of the quality characteristics of the product, even when physical measurements of product quality are not available. VM can thus bring significant production benefits, including improved process control, reduced quality losses and higher productivity. In this dissertation, new methods are formulated that will combine long-term performance prediction of sensory signatures from a degrading manufacturing machine with VM quality estimation, which enables integration of predictive condition monitoring (prediction of sensory signatures) with predictive manufacturing process control (predictive VM model). The recently developed algorithm for prediction of sensory signatures is capable of predicting the system condition by comparing the similarity of the most recent performance signatures with the known degradation patterns available in the historical records. The method accomplishes the prediction of non-Gaussian and non-stationary time-series of relevant performance signatures with analytical tractability, which enables calculations of predicted signature distributions with significantly greater speeds than what can be found in literature. VM quality estimation is implemented using the recently introduced growing structure multiple model system paradigm (GSMMS), based on the use of local linear dynamic models. The concept of local models enables representation of complex, non-linear dependencies with non-Gaussian and non-stationary noise characteristics, using a locally tractable model representation. Localized modeling enables a VM that can detect situations when the VM model is not adequate and needs to be improved, which is one of the main challenges in VM. Finally, uncertainty propagation with Monte Carlo simulation is pursued in order to propagate the predicted distributions of equipment signatures through the VM model to enable prediction of distributions of the quality variables using the readily available sensor readings streaming from the monitored manufacturing machine. The newly developed methods are applied to long-term production data coming from an industrial plasma-enhanced chemical vapor deposition (PECVD) tool operating in a major semiconductor manufacturing fab.Mechanical Engineerin

The kinetics of biodegradation of trans-4-methyl-1-cyclohexane carboxylic acid

This thesis presents the study of biodegradation factors of a candidate naphthenic acid compound, the trans isomer of 4-methyl-1-cyclohexane carboxylic acid (trans-4MCHCA). Low molecular weight components of naphthenic acids such as trans-4MCHCA are known to be toxic in aquatic environments and there is a need to better understand the factors controlling the kinetics of their biodegradation. In this study, a relatively low molecular weight naphthenic acid compound and a microbial culture developed in our laboratory (primarily Alcaligenes paradoxus and Pseudomonas aeruginosa) were used to study the biodegradation of this candidate naphthenic acid. The purpose of the research was to evaluate the kinetic parameters and model the biodegradation of this compound in three bioreactor systems: batch reactors, a continuously stirred tank reactor and immobilized cell reactors. In batch reactors, the maximum specific growth rate (0.52±0.04 d-1) of the consortium at 23oC and neutral pH was not highly variable over various initial substrate concentrations (50 to 750 mg/L). Batch experiments indicated that biodegradation can be achieved at low temperatures; however, the biodegradation rate at 4oC was only 22% of that at room temperature (23oC). Biodegradation at various pH values indicated a maximum specific growth rate of 1.69±0.40 d-1 and yield (0.41±0.06 mg/mg) at a pH of 10. Study of the candidate substrate using a continuously stirred tank reactor and the microbial culture developed in the batch experimentations revealed that the kinetics of the candidate naphthenic acid are best described by the Monod expression with a maximum specific growth rate of 1.74±0.004 d-1 and a half saturation constant of 363±17 mg/L. The continuously stirred tank reactor achieved a maximum reaction rate of 230 mg/(L∙d) at a residence time of 1.6 d-1 (39 h).Two high porosity immobilized cell reactors operating continuously over three months were found to consume trans-4MCHCA at a rate almost two orders of magnitude higher than a continuously stirred tank reactor. The immobilized cell systems attained a maximum reaction rate of 22,000 mg/(L∙d) at a residence time of 16 minutes. High porosity immobilized cell reactors were shown to effectively remove a single naphthenic acid substrate in continuously fed operation to dilution rates of 90 d-1. A plug flow model best represented the degradation in the immobilized cell systems and was demonstrated to be a useful tool for studying the effects of parameter variation and prediction of reactor performance. This work highlights the potential of augmented bioremediation systems for the degradation of naphthenic acids