A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Fault Detection and Identification Method Based on Genetic Algorithms to Monitor Degradation of Electrohydraulic Servomechanisms

Electro Hydraulic Actuators (EHAs) keep their role as the leading solution for the control of current generation primary flight control systems: the main reason can be found in their high power to weight ratio, much better than other comparable technologies. To enhance efficiency and reliability of modern EHAs, it is possible to leverage the diagnostics and prognostics disciplines; these two tools allow reducing life cycle costs without losing reliability, and provide the bases for health management of integrated systems, in compliance with regulations. This paper is focused on the development of a fault detection algorithm able to identify the early signs of EHA faults, through the recognition of their precursors and related degradation patterns. Our methodology provides the advantage of anticipating incoming failures, triggering proper alerts for the maintenance team to schedule adequate corrective actions, such as the replacement of the degraded component. A new EHA model-based fault detection and identification (FDI) method is proposed; it is based on deterministic and heuristic solvers able to converge to the actual state of wear of the tested actuator. Three different progressive failure modes were chosen as test cases for the proposed FDI strategy: clogging of the first stage of the flapper-nozzle valve, spool-sleeve friction increase, and jack-cylinder friction increase. A dedicated simulation model was created for the purpose. The results highlighted that the method is adequate in robustness, since EHA malfunctions were identified with a low occurrence of false alarms or missed failures

A Model-Based Prognostic Framework for Electromechanical Actuators Based on Metaheuristic Algorithms

The deployment of electro-mechanical actuators plays an important role towards the adoption of the more electric aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs, in place of more traditional hydraulic solutions, is still set back, due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA prognostics and health management (PHM) system capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The aim of this work is the development of a model-based prognostic framework for PMSM-based EMAs leveraging a metaheuristic algorithm: the evolutionary (differential evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity, and proportional gain) are simulated by a reference model, and then detected and identified by the envisioned prognostic method, which employs a low fidelity monitoring model. The paper findings are analysed, showing good results and proving that this strategy could be executed and integrated in more complex routines, supporting EMAs adoption, with positive impacts on system safety and reliability in the aerospace and industrial field

Compressor valve failure detection and prognostics

Reciprocating compressors are commonly used machinery for industrial applications. Unscheduled downtime and maintenance activity on the compressors causes considerable loss in throughput and efficiency of a plant. Of all the failures that cause unscheduled downtime in reciprocating compressors, valve related causes are predominant. Most of the failures associated with the valves are tracked to the failure of moving elements within the valve. Achieving higher reliability of critical reciprocating systems requires continuously monitoring the system and performing dynamic analysis of the sensory data for valve fault diagnosis. Continuous monitoring will improve the time and cost to repair through keeping a constant vigil for failure events. Though there has been a good amount of work done for condition monitoring of compressors, there has been very little work on detecting and predicting valve failures. The objective of this thesis is to research detection and prediction of valve failures by wavelet analysis, logistic regression and neural network analysis of pressure and temperature signals, which are the most common measurements on a reciprocating compressor system. Valve failures are seeded on a reciprocating compressor testbed that is instrumented with only temperature and pressure sensor order emulate the reciprocating compressor systems used in the industry. The parameters are measured on a continuous basis and baselines are established for normal (or acceptable) behavior and failure (or fault) condition. Deviation of the system from the normal condition and the time for the system to reach the fault mode is quantified with the help of the above mentioned tools. --Abstract, page iii

Methods for many-objective optimization: an analysis

Decomposition-based methods are often cited as the solution to problems related with many-objective optimization. Decomposition-based methods employ a scalarizing function to reduce a many-objective problem into a set of single objective problems, which upon solution yields a good approximation of the set of optimal solutions. This set is commonly referred to as Pareto front. In this work we explore the implications of using decomposition-based methods over Pareto-based methods from a probabilistic point of view. Namely, we investigate whether there is an advantage of using a decomposition-based method, for example using the Chebyshev scalarizing function, over Paretobased methods

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Universal Approximators for Direct Policy Search in Multi-Purpose Water Reservoir Management: A Comparative Analysis

open5This study presents a novel approach which combines direct policy search and multi-objective evolutionary algorithms to solve high-dimensional state and control space water resources problems involving multiple, conflicting, and non-commensurable objectives. In such a multi-objective context, the use of universal function approximators is generally suggested to provide flexibility to the shape of the control policy. In this paper, we comparatively analyze Artificial Neural Networks (ANN) and Radial Basis Functions (RBF) under different sets of input to estimate their scalability to high-dimensional state space problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while the ANN solutions are often Pareto-dominated by the RBF ones.Matteo Giuliani; Emanuele Mason; Andrea Castelletti; Francesca Pianosi; Rodolfo Soncini SessaGiuliani, Matteo; Mason, Emanuele; Castelletti, ANDREA FRANCESCO; Pianosi, Francesca; SONCINI SESSA, Rodolf

Quantifying dynamic sensitivity of optimization algorithm parameters to improve hydrological model calibration

It is widely recognized that optimization algorithm parameters have significant impacts on algorithm performance, but quantifying the influence is very complex and difficult due to high computational demands and dynamic nature of search parameters. The overall aim of this paper is to develop a global sensitivity analysis based framework to dynamically quantify the individual and interactive influence of algorithm parameters on algorithm performance. A variance decomposition sensitivity analysis method, Analysis of Variance (ANOVA), is used for sensitivity quantification, because it is capable of handling small samples and more computationally efficient compared with other approaches. The Shuffled Complex Evolution method developed at the University of Arizona algorithm (SCE-UA) is selected as an optimization algorithm for investigation, and two criteria, i.e., convergence speed and success rate, are used to measure the performance of SCE-UA. Results show the proposed framework can effectively reveal the dynamic sensitivity of algorithm parameters in the search processes, including individual influences of parameters and their interactive impacts. Interactions between algorithm parameters have significant impacts on SCE-UA performance, which has not been reported in previous research. The proposed framework provides a means to understand the dynamics of algorithm parameter influence, and highlights the significance of considering interactive parameter influence to improve algorithm performance in the search processes.National Natural Science Foundation of ChinaChina Scholarship Counci