Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme

Development of a methodology for the diagnosis of internal combustion engines using non-invasive measurements based on the use of interpretable neural networks applicable to databases with multiple annotators

Pressure is one of the essential variables that give information for engine condition and monitoring. Direct recording of this signal is complex and invasive, while the angular velocity can be measured easily. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. On the other hand, the increasing popularity of crowdsourcing platforms, i.e., Amazon Mechanical Turk, changes how datasets for supervised learning are built. In these cases, instead of having datasets labeled by one source (which is supposed to be an expert who provided the absolute gold standard), databases holding multiple annotators are provided. However, most state-of-the-art methods devoted to learning from multiple experts assume that the labeler's behavior is homogeneous across the input feature space. Besides, independence constraints are imposed on annotators' outputs. This document presents a Regularized Chained Deep Neural Network to deal with classification tasks from multiple annotators. In this thesis, we develop 2 strategies aiming to avoid intrusive techniques that are commonly used to diagnose Internal Combustion Engines (ICE). The first consist of a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter to estimate the in-cylinder pressure of a single-cylinder ICE from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN's delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2>0.9, avoiding complicated pre-processing steps. The second technique, termed RCDNN, jointly predicts the ground truth label and the annotators' performance from input space samples. In turn, RCDNN codes interdependencies among the experts by analyzing the layers' weights and includes l1, l2, and Monte-Carlo Dropout-based regularizers to deal with the overfitting issue in deep learning models. Obtained results (using both simulated and real-world annotators) demonstrate that RCDNN can deal with multi-labelers scenarios for classification tasks, defeating state-of-the-art techniques.La presión es una de las variables esenciales que dan información para el estado del motor y su monitorización. El registro directo de esta señal es complejo e invasivo, mientras que la velocidad angular puede medirse fácilmente. No obstante, el reto consiste en predecir la presión del cilindro utilizando la cinemática del eje con precisión. Por otro lado, la creciente popularidad de las plataformas de crowdsourcing, por ejemplo, Amazon Mechanical Turk, cambia la forma de construir conjuntos de datos para el aprendizaje supervisado. En estos casos, en lugar de tener conjuntos de datos etiquetados por una sola fuente (que se supone que es un experto que proporcionó el estándar de oro absoluto), se proporcionan bases de datos con múltiples anotadores. Sin embargo, la mayoría de los métodos de vanguardia dedicados al aprendizaje a partir de múltiples expertos suponen que el comportamiento del etiquetador es homogéneo en todo el espacio de características de entrada. Además, se imponen restricciones de independencia a los resultados de los anotadores. Este documento presenta una Red Neuronal Profunda Encadenada Regularizada para abordar tareas de clasificación a partir de múltiples anotadores. En esta tesis, desarrollamos dos estrategias con el objetivo de evitar las técnicas intrusivas que se utilizan habitualmente para diagnosticar motores de combustión interna (ICE). La primera consiste en una red neuronal de retardo temporal (TDNN), interpretada como un filtro de respuesta de pulso finito (FIR) para estimar la presión en el cilindro de un ICE de un solo cilindro a partir de las fluctuaciones de la velocidad angular del eje. Los experimentos se realizan sobre datos obtenidos de un ICE que opera en 12 estados diferentes cambiando la velocidad angular y la carga. El retardo de la TDNN se ajusta para obtener la mayor puntuación posible basada en la correlación. Nuestra metodología puede predecir la presión con un R2>0,9, evitando complicados pasos de preprocesamiento.MaestríaMagíster en Ingeniería EléctricaContent 1 Introduction 10 1.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.2 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.1 General objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3.2 Specific objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2 TDNN-based Engine In-cylinder Pressure Estimation from Shaft Velocity Spectral Representation 18 2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.1 Time Delay Neural Network fundamentals . . . . . . . . . . . . . . . 19 2.2.2 Harmonic prediction performance based on Magnitude-Squared Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.1 Engine Measurements, Data Acquisition, and Preprocessing . . . . . 22 2.3.2 Pressure signal estimation . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 Master Thesis: Content 3 Regularized Chained Deep Neural Network Classifier for Multiple Annotators 37 3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3.1 Tested datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3.2 RCDNN detailed architecture and training . . . . . . . . . . . . . . . 46 3.3.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.3.4 Introducing spammers and malicious annotators . . . . . . . . . . . . 55 3.3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4 Final Remarks 58 4.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.1.1 TDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.1.2 RCDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.1 TDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.2 RCDNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.3 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Fault Diagnosis Via Univariate Frequency Analysis Monitoring: A Novel Technique Applied to a Simulated Integrated Drive Generator

The purpose of this research was to develop a fault detection and diagnostic method that would be able to detect and isolate seeded faults in data that was generated from a simulated integrated drive generator. The approach to the solution for this problem is summarized below. A novel approach for the detection and diagnoses of an anomaly due the occurrence of a fault within a system has been developed. This innovative technique uses specific characteristics of the frequency spectrum of a univariate signal to monitor system health for abnormal behavior due to previously characterized component failure. A fault detection and diagnostic scheme was developed that used dual heteroassociative kernel regression models. The first of these empirical models estimates selected features from the analytical redundant spectrum characteristic profile of the exciter current using power demand, a stressor, placed on the system as input query. The predicted spectrum features were compared to the actual characteristic features, which resulted in the generation of a residual signal. This signal was then analyzed in order to determine if they were the result of normal system disturbances or a predefined fault. If a fault was detected, the residual signal was passed to the second model, which isolated, and given enough information, identified the specific component of components causing the anomaly. Two case studies are presented to illustrate the capability to detect, isolate, and identify a system anomaly. As demonstrated, the monitoring of the frequency spectrum of a single variable can provide adequate indication of equipment health. With the availability of the appropriate data, as in the first case, it is possible for the development of three-layer detection and diagnostic systems that provides fault detection, isolation, and identification. A three-layer detection and diagnostic system is essential in the development of more advance health monitoring and prognostic systems. Despite some shortcomings in the simulated data made available for this work, this method is believed to be applicable to data that more realistically captures real-world relationships, including sensor noise and faults that grow with time

An Integrated Fuzzy Inference Based Monitoring, Diagnostic, and Prognostic System

To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable grab bag of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ~45 hours and 95% CIs of ~162 hours

Real-time fault identification for developmental turbine engine testing

Hundreds of individual sensors produce an enormous amount of data during developmental turbine engine testing. The challenge is to ensure the validity of the data and to identify data and engine anomalies in a timely manner. An automated data validation, engine condition monitoring, and fault identification process that emulates typical engineering techniques has been developed for developmental engine testing.An automated data validation and fault identification approach employing enginecycle-matching principles is described. Engine cycle-matching is automated by using an adaptive nonlinear component-level computer model capable of simulating both steady state and transient engine operation. Automated steady-state, transient, and real-time model calibration processes are also described. The model enables automation of traditional data validation, engine condition monitoring, and fault identification procedures. A distributed parallel computing approach enables the entire process to operate in real-time.The result is a capability to detect data and engine anomalies in real-time during developmental engine testing. The approach is shown to be successful in detecting and identifying sensor anomalies as they occur and distinguishing these anomalies from variations in component and overall engine aerothermodynamic performance. The component-level model-based engine performance and fault identification technique of the present research is capable of: identifying measurement errors on the order of 0.5 percent (e.g., sensor bias, drift,level shift, noise, or poor response) in facility fuel flow, airflow, and thrust measurements; identifying measurement errors in engine aerothermodynamic measurements (rotorspeeds, gas path pressures and temperatures); identifying measurement errors in engine control sensors (e.g., leaking/biased pressure sensor, slowly responding pressure measurement) and variable geometry rigging (e.g., misset guide vanes or nozzle area) that would invalidate a test or series of tests; identifying abrupt faults (e.g., faults due to domestic object damage, foreign object damage, and control anomalies); identifying slow faults (e.g., component or overall engine degradation, and sensor drift). Specifically, the technique is capable of identifying small changes in compressor (or fan) performance on the order of 0.5 percent; and being easily extended to diagnose secondary failure modes and to verify any modeling assumptions that may arise for developmental engine tests (e.g., increase in turbine flow capacity, inaccurate measurement of facility bleed flows, horsepower extraction, etc.).The component-level model-based engine performance and fault identification method developed in the present work brings together features which individually and collectively advance the state-of-the-art. These features are separated into three categories: advancements to effectively quantify off-nominal behavior, advancements to provide a fault detection capability that is practical from the viewpoint of the analysis,implementation, tuning, and design, and advancements to provide a real-time fault detection capability that is reliable and efficient

Dynamic predictive reliability assessment of ship systems

Recent research shows that maritime industry has adopted innovative and sophisticated inspection and maintenance practices. A flexible framework, applicable on complex machinery, is introduced towards ship maintenance. A holistic inspection and maintenance notion is implemented, introducing different strategies, methodologies, and tools, suitably selected, for each required ship system. The proposed framework enables predictive reliability assessment of ship machinery, while scheduling maintenance actions by enhancing safety and systems' availability. This paper presents the Probabilistic Machinery Reliability Assessment (PMRA) strategy, which achieves predictive reliability assessment and evaluation of different complex ship systems. The assessment takes place on system, subsystem and component level, while allowing data fusion of different data types from various input sources. In this respect, the combination of data mining method (k-means), manufacturers' alarm levels, dynamic state modelling (Markov Chains), probabilistic predictive reliability assessment (Dynamic Bayesian Belief Networks) and qualitative decision making (Failure Modes and Effects Analysis) is suggested. PMRA has been clearly demonstrated in a case study on selected ship machinery. The results identify the most unreliability systems, subsystems and components, while advising practical maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme