Prognosis and health management

The papers published in these proceedings are presented in the Third International Seminar on Maintenance, Condition Monitoring and Diagnostics, to be arranged in Oulu, Finland, in 29th – 30th September, 2010.

Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present seminar is supported by a variety of Finnish industrial enterprises.

ISBN 978-951-98113-5-2

The reliable condition monitoring of machines and the early detection of faults play an important role in condition-based maintenance. Information on the condition of machines is needed in different forms but finally it has to be in as simple form as possible so that it can be utilised in the decision-making by the maintenance and management personnel. In the field of machine condition monitoring, there is a wide range of signal processing methods, which are used in feature extraction and fault diagnosis. Differentiation and integration are very common and important operations in vibration signal processing. The order of derivative is typically an integer number but it can also be any fractional number. Especially in the challenging fault and process cases, signals whose order of derivative is a real or complex number could be clearly more sensitive in fault detection than the commonly used signals: displacement, velocity and acceleration. In this paper, the application of complex o...

One of the complex processes in spectral analysis of surface waves (SASW) data analysis is the inversion procedure. An initial soil profile needs to be assumed at the beginning of the inversion analysis, which involves calculating the theoretical dispersion curve. If the assumption of the starting soil profile model is not reasonably close, the iteration process might lead to nonconvergence or take too long to be converged. Automating the inversion procedure will allow us to evaluate the soil stiffness properties conveniently and rapidly by means of the SASW method. Multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and linear regression (LR) algorithms were implemented in order to automate the inversion. For this purpose, the dispersion curves obtained from 50 field tests were used as input data for all of the algorithms. The results illustrated that SVR algorithms could potentially be used to estimate the shear wave velocity of soil.

Integrated System Health Management (ISHM) is a promising technology that fuses sensor data and historical state-of-health information of components and subsystems to provide actionable information and enable intelligent decision-making regarding the operation and maintenance of aerospace systems. ISHM fundamentally relies on assessments and predictions of system health, including the early detection of failures and estimation of Remaining Useful Life (RUL). Model-based, data-driven or hybrid reasoning techniques can be utilized to maximise the timeliness and reliability of diagnosis and prognosis information. The benefits of ISHM include enhancing the maintainability, reliability, safety and performance of systems. The next evolution of the ISHM concept, Intelligent Health and Mission Management (IHMM), delves deeper into the utilization of on-line system health predictions to modify mission profiles to ensure safety and reliability, as well as efficiency through predictive integ...

Industrial condition monitoring is typically performed by experts working in a maintenance organisation. Especially in the process industry the roles of the maintenance personnel and operators are highly separated, which in many cases lead to communication discrepancies that can impair the efficiency of the condition monitoring programme. Concepts such as TPM and its close relative LEAN emphasise the need for operator involvement in maintenance (1, 2). Regardless of the efforts, the concepts of operator maintenance or Autonomous Maintenance (AM) have not become widely accepted in the process industry. By combining operators' daily machinery checks and condition monitoring, it is possible to create a natural communication channel between operators and condition monitoring experts for improved efficiency. The approach known as Operator Driven Reliability (ODR) utilises modern portable technology to enable operators to check and record machinery data as an integral part of their daily operator rounds (3). This includes visual machinery checks but also some relatively simple vibration and temperature measurements. The data are collected and stored in a database that can be accessed both by operators and the maintenance personnel. It is important to understand that technology is just an enabler for creating systematic and effective work processes. Therefore, it is vital that operator checks are carefully planned in order to support the effectiveness of production and maintenance.

The chronic kidney disease is the loss of kidney function. Often time, the symptoms of the disease is not noticeable and a large number of lives are lost every year due to the disease. Using machine learning algorithm for medical studies, the disease can be predicted with a high accuracy rate and a very short time. Using four of the supervised classification learning algorithms, i.e., logistic regression, Decision tree, Random Forest and KNN algorithms, the prediction of the disease can be done. In the paper, the performance of the predictions of the algorithms are analyzed using a pre-processed dataset. The performance analysis is done base on the accuracy of the results, prediction time, ROC and AUC Curve and error rate. The comparison of the algorithms will suggest which algorithm is best fit for predicting the chronic kidney disease.

Separation of solids from fluid is a vital process to achieve the desired level of purification in industry. Contaminant filtration is a common process in a variety of applications in industry. Clogging of filter phenomena is the primary failure mode leading to replacement or cleansing of filter. Reduced performance and efficiency or cascading failures are the unfortunate outcomes of a clogged filter. For instance, solid contaminants in fuel may lead to performance reduction in the engine and rapid wear in the fuel pump. This paper presents the development of an experimental rig to collect accelerated filter clogging data and a physics-based degradation model to represent the filter clogging. In the experimental rig, pressure drop across the filter, flow rate, and filter mesh images are acquired during the accelerated clogging experiments. The pressure drop across the filter due to deposition of suspended solids in the liquid is modelled and employed in the degradation modelling. Then, the physics based degradation model simulated using MatLab is compared with the real clogging data and the effectiveness of the degradation model is evaluated.

Real-time vibration monitoring and diagnostics system is developed and supplied for the 5 stand 4-h tandem cold rolling mill 2030 NLMC. Mechanism of chatter regeneration due to high frequency strip thickness variation and stands synchronization in the tandem rolling mills are considered. Methods for chatter passive damping and active suppression are considered. Work rolls hydraulic bending system influence on bearings vibration and chatter is discussed. The new methods are used for the 3rd octave chatter early diagnostics and model based mill control.

Condition monitoring enables reliable and economical way of action for maintenance operations in modern industrial plants. Increasing number of measurement points and more demanding problems require automatic fault detection. Advanced signal processing methods expose failures earlier and provide information on suitable operating conditions of machines. Intelligent methods have been increasingly used in model based fault diagnosis and intelligent analysers. These methods provide various techniques for combining a large number of features.

Lime kilns are large machines, approximately 4 meters in diameter and even more than 100 meters long, with very slow rotational speeds. Depending on production conditions, the kiln must run at different production capacity and rotational speed. The speed is controlled together with the fuel feed and the draught fan speed to obtain good operating conditions. Temperatures in the hot end are very high.

Alignment problems of the kiln are severe because of the high weight affecting on the supporting rolls. Problems may lead to serious damages or even to fire. The situations are very bad for the whole production since lime kilns are essential parts of the chemical recovery cycle of a pulp mill. The chemical pulp production is strongly integrated, and a smooth operation is achieved if all the subprocesses are operating well. Condition monitoring is getting more and more important as the period of continuous operation of the pulp plant is planned to be increased to 18 months. Earlier there have been usually three maintenance shut-downs during a year.

In this paper, a large set of previously collected measurements was analysed with intelligent models based on new features. The acceleration measurements were done on the bearing housing of the supporting rolls. This 97.5 meter long kiln has eight supporting rolls with sleeve bearings, and there are two measurement points for each roll. The rotation time of the kiln is from 42 to 45 seconds, and a rotation of a supporting roll takes from 12.2 to 13.1 seconds.

The monitoring systems presently available for vibration observation of civil engineering struc-tures are normally based on networks of accelerometers and strain gages distributed along the structure connected to one or more central computer. 1, 2 This computer launches the acquisition and stores and transmits the collected data. Depending on the dimensions of the structure and the extensiveness of the monitoring system, one common issue with most widely used monitoring systems is the use of electrical cables to power sensors and ...

The numerical simulation using Finite Element (FE) method is a main activity in the design - validation process of mechanical products. Using this method and CAD tools can give an efficient advantage in the CAD - Analysis loop. The geometric model (mesh) used in the FE method is mainly obtained from the CAD model (CSG, B-Rep). In order to reduce the analysis time without bringing precision on computation results, we propose to integrate a new link in the design and analysis interoperability process. This link is named Idealization or simplification of CAD model. So, the automation of the idealization task is important in the CAD-Analysis integration process. In this paper, we propose an idealization approach which mainly depends on the geometry, the solicitations and the behavior of material. According to the analysis goal, the designer can have several alternatives of analysis models which depend on the level of eliminated details and the abstraction level (1D, 2D or 3D). The non manifold models should not be a barrier to the considered analysis model. We start with a positioning of several geometry simplification methods for CAD models in order to generate analysis models adapted with the considered analysis point of view. Based on this state of the art, we present the idealization approach in an integrated design context. Then, we propose our general method of the CAD geometry idealization. This one is based on an original algorithm witch integrate tow alternatives approach (selective approach and automatic approach). In order to emphasize our contribution, we present some examples of idealized form features (parallelepiped, cylinder, and wedge) implemented with OpenCacade code. Finally, an example of mechanical part using implemented form features is developed. The output of the algorithm is some proposed analysis models based on the CAD model of the part.

http://www.designsociety.org/publication/25383/idealization_of_cad_geometry_using_design_and_analysis_integration_features_models

Dans le but de concevoir un système de diagnostic des défauts d'un moteur diesel 4 temps à combustion interne, l'incidence d'un défaut de distribution sur la vibration du moteur a été étudié. Ce défaut a été simulé en modifiant les jeux entre les cames et les poussoirs en contact avec les queues des soupapes.

The relentless competition among automotive companies and increased demands from customers for driver assistance functions, dynamically-controlled safety systems, driving comfort and infotainment services in vehicles are creating mounting time-to-market pressures and, consequently, shortened development times. With the shortened development times and increased vehicle complexity, guaranteeing hardware-software integrity and, hence, vehicle performance has become a salient issue. This is because poor vehicle performance increases maintenance costs to customers and warranty costs to automotive manufacturers; which, in turn, result in customer dissatisfaction and reduced competitiveness of US automotive industry. The development of methods for enhancing the reliability of embedded systems of heterogeneous components that are designed and implemented by different suppliers, and vehicle diagnostics and prognostics are of vital interest to US automotive industry.

Current emphasis on improving maintenance policies and techniques while decreasing overall Life Cycle Costs (LCCs) has driven industry to develop unique and potential cost saving philosophies such as Condition Based Maintenance (CBM), a subset of Reliability Centered Maintenance (RCM).  As the government has taken notice, CBM policies have been directed for mission-critical systems.  Of particular interest and focus are the Performance Based Logistics (PBL) contracts, which reward industry for achieving government requirements by improving profitability.  CBM techniques can be utilized inside a PBL contract as a way to reduce life cycle costs, but incentives for this be long-term goal are minimal for PBL and implementing CBM to achieve this goal can be cost prohibitive for contractors.  This tutorial presents a history of CBM, tools, techniques, and the elements, presents an example of CBM analysis and its benefits, discusses the government CBM and PBL contracting policies, and presents a proposed way for government and industry to both optimize profitability of PBL contracting for industry and decrease LCCs for the government with the utilization of CBM strategies.

The papers published in these proceedings are presented in the International Conference on Maintenance, Condition Monitoring and Diagnostics, and Maintenance Performance Measurement and Management, MCMD 2015 and MPMM 2015, to be arranged in Oulu, Finland, in 30th September – 1st October, 2015.

Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present conference is supported by a variety of Finnish industrial enterprises.

ISBN 978-951-98113-7-6

D e c e m b e r 2 0 0 8 The ultimate measure of a man is not where he stands in moments of comfort and convenience, but where he stands at times of challenge and controversy (Martin Luther King) -This thesis is dedicated to who has made me a man

Even though prognostics has been defined to be one of the most difficult tasks in Condition Based Maintenance (CBM), many studies have reported promising results in recent years. The nature of the prognostics problem is different from diagnostics with its own challenges. There exist two major approaches to prognostics: data-driven and physics-based models. This paper aims to present the major challenges in both of these approaches by examining a number of published datasets for their suitability for analysis. Data-driven methods require sufficient samples that were run until failure whereas physics-based methods need physics of failure progression.

The impact that progress in information technology has on the entire activity of humans has proportions and a complexity that are difficult to describe or summarize. On one hand, the objects built by humans become – more and more each day – “smart”, or at least capable of collecting, processing and transmitting information.  However, these objects are increasingly capable of making decisions, based on pre-programmed algorithms or based on heuristic “learning” techniques – through progressive exposure to the environment and the preferences and options of the user. On the other hand, all of the intellectual and even emotional activity of modern man is increasingly assisted and dependent on instruments developed through information technology: databases, search engines, communication devices, operational models, heuristic models. 

This progress in information technology has submitted the actual paradigm of scientific knowledge to revision pressures unprecedented from Karl Popper to the present day. The new debate has coagulated like a show around the confrontation between Noam Chomski, the illustrious American linguist and political scientist and Peter Norvig, the leader of the research department in Google company. This confrontation of ideas began in 2011, at the Brains, Minds, and Machines symposium hosted by the Massachusetts Institute of Technology, from „how one can obtain artificial intelligence” and, in a few months, through articles and relatively elaborate interventions [Norvig, 2012] became about „what human knowledge is”. Essentially, it is about knowledge understood as an explicative model, simple enough for every link to be logically comprehensible (Chomski) and, alternatively, knowledge as an informational model, a „black box”, capable of offering increasingly better predictions based on historical data and internal statistical search algorithms (Norvig). The question is: does the progress of knowledge still depend on models that are elegant in their simplicity, logically accessible – in integrity and in detail, models that are continually reviewed to approximate the available data as well as possible? Or are we entering a new era of statistical informational models, capable of continuously processing enormous quantities of data – historical or real time data and providing classifications, predictions, etc. – but without subjacent explanations: we find out how much, when, with associated probabilities, but the question „why?” loses its relevance.

In the applied medical sciences, the most significant reflection of this pressure to change the relationship paradigm between historical data – formalized knowledge – the practice of decisions and predictions is Evidence Based Medicine, EBM. It is not a new concept – its origins can be traced (at least) to the comparative clinical study on six groups of patients, through which the British physician James Lind, of the Royal Navy, established in 1747 what the foods than can prevent and treat scurvy – lemons and oranges – were. Isolating vitamin C and clarifying the pathogenesis of scurvy came about almost 200 years later, in 1932, but the prophylactic treatment had already been generalized by the Royal Navy as early as 1787, based on the evidence collected by Lind. The reference definition of EBM was settled in 1996:
„the conscious, explicit and judicious use of the best evidence in order to formulate decisions with regard to the treatment of individual patients” [Sackett, 1996]. Best evidence meaning proof (as certain as possible), not explanations (as plausible as possible). In the last few decades, the scope of medical research and the increasing pressure for the maximum but responsible use of the results of this research imposed EBM as a dominant technique for collecting, organizing, evaluating and using medical information; EBM procedures are now standardized at a national and international level, with specialized associations and regulation and certification organizations facilitating the development of work methods [Mayer, 2010].

In a good correlation with EBM, a direction of accelerated progress in applying new information techniques in medicine is that of prediction models capable of continuous development, through what is suggestively known as „learning” or „training”. Artificial neural networks are one of the forms that were intensely studied and tested in this context, as part of the research surrounding „artificial intelligence”. Promising results have been reported more and more frequently in applying neural networks to diagnosis [Ganesan, 2010], [Jiang, 2010], [Barwad, 2012], [Amato, 2013], but also to evaluating prognosis and prediction factors [Lundin, 1999]. The number of such factors under evaluation has risen exponentially lately, especially due to the progress in genomics and molecular biology, which gave rise to a necessity for new information instruments, capable of processing such a large data volume.  Prediction based on such information models supplied with huge databases is already transitioning into the current medical practice [Silva, 2015], [Prigg, 2015]: at Beth Israel Deaconess Hospital in Boston USA, Dr. Steven Horng recently announced the operational phase for of an information instrument that allows the formulation of a diagnosis, indicates recommended therapeutic procedures and estimates the prognosis for every patient in intensive therapy.  Dr. Horng, a physician specializing in both emergency medicine and biomedical IT, believes that the accuracy with which the program formulates, for example, the prediction for the imminence of the patient’s death in the next 30 days is over 96%.  The recent and historical clinical and paraclinical data of the patient are introduced into the information model based on similar historical data collected in the last 30 years from over 250000 patients. The database is augmented continuously through the regular collection of information, every 3 minutes, from the physiological data monitors installed for all the patients admitted into the ICU of the hospital. 

In the last 30 years, technological progress has brought about a true revolution in medical imagery [Dougherty, 2009]. On one hand, minimally invasive investigation techniques have become accessible for a very large number of patients, both in human and veterinary medicine: MRIs, computerized tomography, nuclear magnetic resonance, confocal reflection microscopy. On the other hand, the increased performance of information instruments facilitates new approaches in distinguishing useful information in medical images [Bubnov, 2014], [Lennon, 2015]. The digital format, practically generalized both for the results in new investigation techniques, as well as for classical microscopy and radiology, now facilitates processing for the extraction of useful information, storing and accessing images, as well as the comparison, morphometric and statistical analysis, identification of regions of interest/lesions etc. [Rangayyan, 2005].

The road towards semi-automated and even automated procedures that can be used directly in research, screening, pre-diagnosis or diagnosis is open and is beginning to be intensely exploited, including by the commercial component of medical activity. Using automated and semi-automated procedures also leads to an exponential increase in the volume of collected data which, in turn, reclaims information approaches such as neural networks for processes or such as EBM for testing and application. It is a progress spiral that is presently accelerating and in which man, the professional, the expert, can be centrifugally marginalized towards positions in which he only contributes punctually to ample, interdependent processes that he does not control anymore. It is possible that we are witnessing the dawn of a new historical era, in which the virtual reality created continuously by humans becomes as significant and autonomous as objective reality. Our attempt to know and change reality in its entirety for our own purposes shall inevitably adapt to the new situation. 

Simon Rosenfeld, from the National Cancer Institute – USA, considers that oncology and oncological research are at a critical crossroads with the dynamics of nonlinear systems and research regarding swarm intelligence in the contemporary attempt to offer a new, unifying vision with regard to the way in which systems as diverse as „biological networks, communities of social insects, robotic communities, microbial communities, communities of somatic cells, (…) social networks, and (…) many other systems” [Rosenfeld, 2015] appear and function. Rosenfeld’s unifying vision marks an essential link between carcinogenesis, fractal analysis – that was mainly developed as a modeling instrument for nonlinear dynamic systems and artificial neural networks – that are one of the best documented examples of man-made swarm intelligence.

Fractal analysis facilitates the obtaining of measurements, of numbers that quantify the non-periodical complexity that is so characteristic of biological structures, on all size scales in which life is manifested.  This research has investigated the possibility of including fractal analysis applied to histopathological or cytopathological microscopic images amongst the instruments used for diagnosing tumours in animals. I then tested the introduction of the fractal dimension amongst oncological prognostic factors. For this purpose, I developed statistical models and artificial neural network-type models for the survival duration, including the fractal dimension in the set of predictors based on which these models estimate the prognostic for cancer patients.

The papers published in these proceedings are presented in the Second International Seminar on Maintenance, Condition Monitoring and Diagnostics, to be arranged in Oulu, Finland, in 28th – 29th September, 2005.

Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present seminar is supported by a variety of Finnish industrial enterprises.

ISBN 951-98113-3-8

‫پژمان‬ ‫چرانی‬ ‫رضایتی‬ -‫ارشد‬ ‫کارشناسی‬ ‫دوره‬ ‫مدرس‬ ‫تربیت‬ ‫دانشگاه‬ ، ‫راهنما:‬ ‫استاد‬ ‫دکتر‬ ‫سعید‬ ‫نجفی‬ ‫کاظمی‬ ‫گزارش‬ ‫چوب‬ ‫صنایع‬ ‫در‬ ‫مخرب‬ ‫غیر‬ ‫آزمونهای‬ ‫کاربرد‬ ‫درس‬ -3131 ‫گزارش‬ ‫چوب‬ ‫صنایع‬ ‫در‬ ‫مخرب‬ ‫غیر‬ ‫آزمونهای‬ ‫کاربرد‬ ‫درس‬ -3131 2 ‫گزارش‬ ‫چوب‬ ‫صنایع‬ ‫در‬ ‫مخرب‬ ‫غیر‬ ‫آزمونهای‬ ‫کاربرد‬ ‫درس‬ -3131 3 ‫گزارش‬ ‫چوب‬ ‫صنایع‬ ‫در‬ ‫مخرب‬ ‫غیر‬ ‫آزمونهای‬ ‫کاربرد‬ ‫درس‬ -3131 5 ‫گزارش‬ ‫چوب‬ ‫صنایع‬ ‫در‬ ‫مخرب‬ ‫غیر‬ ‫آزمونهای‬ ‫کاربرد‬ ‫درس‬ -3131 21

The condition monitoring and diagnosis of machinery can today take place at any time and from any location. With web-based access to condition monitoring analysis applications and the latest condition monitoring equipment, it is possible to remotely monitor local or more widely distributed equipment. Remote monitoring services allow new operational possibilities in condition monitoring. For instance, asset owners can monitor unmanned and even very remote sites, such as hydro or wind turbines. With remote technology, different stakeholders can have access to data, while specialists from different fields can take part in diagnostics and problem solving cases. By centralising the monitoring process, it is possible to standardise and synchronise procedures for more efficient predictive maintenance. Remote monitoring services play an increasingly important role in the efforts of a modern company to institute high-quality Predictive Maintenance (PdM). Such services can offer not only increased assurance, but also flexibility. Among other things, it is possible to select a simple hosted application, or a more expansive approach that utilises the knowledge base of an experienced provider. At the same time, this enables companies to avoid the costs and other issues involved in setting up an in-house program. This paper describes the concept of remote monitoring through practical applications. It gives an overview of why and how the concept has been taken into use in industry and what the results have been. Some ideas for future development will also be highlighted.

The purpose of this study was to reveal how and with whom familly members of terminal lung cancer patients consult.  Semi-structured interviews were conducted with 5 family members of lung cancer patients who were entering the palliative care from aggressive medical treatments.  Their narrartive data were collected and analyzed quantitatively and qualitatively by text mining methods.  The frequency of characteristic words and phrases were obtained. Then original narrative sentences were referred in terms of frequently appeared words and phrases.  Based on these analyses the following results were extracted.  Some family members recognized that they were isolated from other family members and experienced the feelings of helplessness.  Nursing professionals should pay devote greater attention to the psychological isolation of family members and actively support the patients.  More concretely, nurses should engage in active listening and encourage family members to share their psychological burden and pain. These findings can be applied to every day practice as clinical techniques by nurses working at the unit for cancer patients.

The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cutoff frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.

The detection of misfire in an internal combustion diesel engine by vibration analysis has been investigated. In the experimental study, a diesel engine with six cylinders in line and cylinder head in 6 parts has been used. Vibration signal has been measured, successively, at seven positions of engine structure and analyzed in the time domain to extract statistical features sensitive to the engine misfire.

An ambient vibration survey of the Humber Bridge was carried out in July 2008 by a combined team from the UK, Portugal and Hong Kong. The exercise had several purposes that included the evaluation of the current technology for instrumentation and system identification and the generation of an experimental dataset of modal properties to be used for validation and updating of finite element models for scenario simulation and structural health monitoring. The exercise was conducted as part of a project aimed at developing online diagnosis capabilities for three landmark European suspension bridges.

En el presente artículo se recorren investigaciones en curso sobre la inferencia clínica y la intervención en psicoterapia, con el objetivo de describir una posible vía de vinculación entre las intervenciones psicoterapéuticas y las inferencias pronósticas. Se mencionan estudios sobre intervención clínica y trabajos de clasiflcación de intervenciones. Se describen distintas líneas de investigación posibles para la noción de pronóstico en psicoterapia, y se proponen modalidades y criterios para su evaluación, tomando la investigación sobre inferencia clínica como eje ordenador. Se mencionan, a su vez, resultados disponibles sobre el estudio de la inferencia pronóstica. Las nociones de inferencia clínica, inferencia pronóstica e intervención son deflnidas y analizadas como partes del proceso psicoterapéutico. Se propone una estrategia de vinculación entre las inferencias pronósticas y las intervenciones. La cuestión del marco teórico del psicoterapeuta es presentada como factor en juego tanto en la producción inferencial como en la modalidad de intervención en psicoterapia.

During the last years, a number of techniques aimed at the experimental identification of the dynamic characteristics of structures has been developed. Their use has progressively extended to various fields, such as aerospace, automotive and civil structures. Besides the traditional techniques based on input knowledge, increasing interest is observed for techniques based on environmental vibrations that do not need any expensive excitation device.

One of the most important activities of medicine is the act of prognostication. The success of medical practice depends indeed also on the ability of physicians to make reliable and accurate predictions on the course of diseases and patients' health. However, traditionally the concept of prognosis has been partially overlooked both by clinicians and philosophers of science. In this chapter we aim to fill this gap, focusing on the logical and epistemological aspects of prognostic reasoning, that has been usually understood in terms of abduction. In fact, any prognosis has to deal with some kind of uncertainty and abductive forms of reasoning seem to nicely cope with it. The main aim of this chapter is to summarize and critically discuss all the major findings directly related to prognostic and abductive reasoning in both philosophical and scientific literature.

Industrial maintenance has been facing major changes during the last decades. Traditionally the industrial maintenance was carried out by own resources, often the same ones performing the operations. As the processes and equipment developed, the need for more specialised maintenance knowhow become evident and maintenance become the profession of its own. When the complexity of maintenance increased the maintenance organisations in industry become more and more sophisticated and specialised maintenance service companies started to emerge. These companies started to offer outsourced maintenance and they were often able to increase the maintenance efficiency with their optimised maintenance practices and advanced methodologies. The concepts are still developing. One of the most important drivers of change today is the rapid development of information and communication technology that makes new networked maintenance models viable. In networked maintenance concept partners bring in their special knowledge for the benefit of all parties. The modern tools allow the best resources to participate decision making instantly regardless of where they are physically located.

Prognostics and health management (PHM) represents a paradigm shift from legacy condition based maintenance (CBM) frameworks by expanding the potentials to accurately and robustly detect and diagnose incipient system faults. The ultimate goal of PHM is reliably predicting Darren McDonnell et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contribute to the overall component degradation. We develop a modelbased prognostics methodology that consists of a joint stateparameter estimation problem, in which the state of a system along with parameters describing the damage progression are estimated, followed by a prediction problem, in which the joint state-parameter estimate is propagated forward in time to predict end of life and remaining useful life. The state-parameter estimate is computed using a particle filter, and is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control algorithm that maintains an uncertainty bound around the unknown parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump that includes damage progression models, to which we apply our model-based prognostics algorithm. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the approach when multiple damage mechanisms are active.

Machines seldom break down suddenly without warning. Therefore, it is crucial to monitor the vibration levels as a function of time. Regression analysis and fuzzy reasoning with triangular episodic representations, which are based on the estimated first and second derivatives of a feature, have been used in trend analysis. Trend indices are calculated from the scaled values by using the means obtained for a short and a long time period. The severity of the situation can be evaluated with a deviation index, which combines the current level, the trend index and the change of the trend index. This index obtains its highest absolute values, when there is considerable difference between the variable and the reference point and this difference continues to grow with increasing speed. The scaling functions are updated in recursive modelling, which is triggered by a fast increase of the indices. Exponential models are used in the real scale. Vibration measurements from two paper machines ar...