Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

PRISM and ENES: a European approach to Earth system modelling

Europe's widely distributed climate modelling expertise, now organized in the European Network for Earth System Modelling (ENES), is both a strength and a challenge. Recognizing this, the European Union's Program for Integrated Earth System Modelling (PRISM) infrastructure project aims at designing a flexible and friendly user environment to assemble, run and post-process Earth System models. PRISM was started in December 2001 with a duration of three years. This paper presents the major stages of PRISM, including: (1) the definition and promotion of scientific and technical standards to increase component modularity; (2) the development of an end-to-end software environment (graphical user interface, coupling and I/O system, diagnostics, visualization) to launch, monitor and analyse complex Earth system models built around state-of-art community component models (atmosphere, ocean, atmospheric chemistry, ocean bio-chemistry, sea-ice, land-surface); and (3) testing and quality standards to ensure high-performance computing performance on a variety of platforms. PRISM is emerging as a core strategic software infrastructure for building the European research area in Earth system sciences. Copyright (c) 2005 John Wiley & Sons, Ltd.

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

Diagnosing Causes of Water Scarcity in Complex Water Resources Systems and Identifying Risk Management Actions

From the water management perspective, water scarcity is an unacceptable risk of facing water shortages to serve water demands in the near future. Water scarcity may be temporary and related to drought conditions or other accidental situation, or may be permanent and due to deeper causes such as excessive demand growth, lack of infrastructure for water storage or transport, or constraints in water management. Diagnosing the causes of water scarcity in complex water resources systems is a precondition to adopt effective drought risk management actions. In this paper we present four indices which have been developed to evaluate water scarcity. We propose a methodology for interpretation of index values that can lead to conclusions about the reliability and vulnerability of systems to water scarcity, as well as to diagnose their possible causes and to propose solutions. The described methodology was applied to the Ebro river basin, identifying existing and expected problems and possible solutions. System diagnostics, based exclusively on the analysis of index values, were compared with the known reality as perceived by system managers, validating the conclusions in all cases

Diagnostics of rolling element bearings for the traction system of high speed trains : experimental evidences

Monitoring of the integrity of rolling element bearings in the traction system of high speed trains is a fundamental operation in order to avoid catastrophic failures and to implement effective condition-based maintenance strategies. Diagnostics of rolling element bearings is usually based on vibration signal analysis by means of suitable signal processing techniques. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in industrial applications, particularly in the field of rail transport, remains scarcely investigated. This paper will address the diagnostics of bearings taken from the service after a long term operation on a high speed train. These worn bearings have been installed on a test-rig, consisting of a complete full-scale traction system of a high speed train, able to reproduce the effects of wheel-track interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is also proposed.

Experimental evidences in bearing diagnostics for traction system of high speed trains

Rolling element bearings are the most critical components in the traction system of high speed trains. Monitoring their integrity is a fundamental operation in order to avoid catastrophic failures and to implement effective condition based maintenance strategies. Generally, diagnostics of rolling element bearings is usually performed by analyzing vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. Several papers have been published on this subject in the last two decades, mainly devoted to the development and assessment of signal processing techniques for diagnostics. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in specific industrial applications, particularly in rail industry, remains scarcely investigated. This paper is aimed at filling this knowledge gap, by addressing the diagnostics of bearings taken from the service after a long term operation on the traction system of a high speed train. Moreover, in order to test the effectiveness of the diagnostic procedures in the environmental conditions peculiar to the rail application, a specific test-rig has been built, consisting of a complete full-scale train traction system, able to reproduce the effects of wheeltrack interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is proposed, in order to limit their number.

Electrical, Thermal and Optical Diagnostics of an Atmospheric Plasma Jet System

Plasma diagnostics of atmospheric plasmas is a key tool in helping to understand processing performance issues. This paper presents an electrical, optical and thermographic imaging study of the PlasmaStream atmospheric plasma jet system. The system was found to exhibit three operating modes; one constricted/localized plasma and two extended volume plasmas. At low power and helium flows the plasma is localized at the electrodes and has the electrical properties of a corona/filamentary discharge with electrical chaotic temporal structure. With increasing discharge power and helium flow the plasma expands into the volume of the tube, becoming regular and homogeneous in appearance. Emission spectra show evidence of atomic oxygen, nitric oxide and the hydroxyl radical production. Plasma activated gas temperature deduced from the rotational temperature of nitrogen molecules was found to be of order of 400 K: whereas thermographic imaging of the quartz tube yielded surface temperatures between 319 and 347 K.

Neural Networks Diagnostics in Homeopath System

We suppose the neural networks for solution the problem of the diagnostic in Homeopath System and consider the algorithms of the training.

Application of the Artificial Intelligence Algorithms for System Analysis of Multi Dimension Physiological Data for Developing Polyparametric Information System of Public Health Diagnostics

The polyparametric intelligence information system for diagnostics human functional state in medicine and public health is developed. The essence of the system consists in polyparametric describing of human functional state with the unified set of physiological parameters and using the polyparametric cognitive model developed as the tool for a system analysis of multitude data and diagnostics of a human functional state. The model is developed on the basis of general principles geometry and symmetry by algorithms of artificial intelligence systems. The architecture of the system is represented. The model allows analyzing traditional signs - absolute values of electrophysiological parameters and new signs generated by the model – relationships of ones. The classification of physiological multidimensional data is made with a transformer of the model. The results are presented to a physician in a form of visual graph – a pattern individual functional state. This graph allows performing clinical syndrome analysis. A level of human functional state is defined in the case of the developed standard (“ideal”) functional state. The complete formalization of results makes it possible to accumulate physiological data and to analyze them by mathematics methods.

The Information-analytical System for Diagnostics of Aircraft Navigation Units

The operation of technical processes requires increasingly advanced supervision and fault diagnostics to improve reliability and safety. This paper gives an introduction to the field of fault detection and diagnostics and has short methods classification. Growth of complexity and functional importance of inertial navigation systems leads to high losses at the equipment refusals. The paper is devoted to the INS diagnostics system development, allowing identifying the cause of malfunction. The practical realization of this system concerns a software package, performing a set of multidimensional information analysis. The project consists of three parts: subsystem for analyzing, subsystem for data collection and universal interface for open architecture realization. For a diagnostics improving in small analyzing samples new approaches based on pattern recognition algorithms voting and taking into account correlations between target and input parameters will be applied. The system now is at the development stage.

“Digital Diagnostics and Communication (DDC) system” Redesigning the Emergency Ambulance

During the development of a new treatment space for the UK emergency ambulance participatory observations with front-line clinicians revealed the need for an integrated patient monitoring, communication and navigation system. The research identified the different information touch-points and requirements through modes of use analysis, day-in-the-life study and simulation workshops with clinicians. Emergency scenario and role-play with paramedics identified 5 distinct ambulance modes of use. Information flow diagrams were created and checked by paramedics and digital User Interface (UI) wireframes were developed and evaluated by clinicians during clinical evaluations. Feedback from clinicians defined UI design specification further leading to a final design proposal. This research was a further development from the 2007 EPSRC funded “Smart Pods” project. The resulting interactive prototype was co-designed in collaboration with ambulance crews and provides a vision of what could be achieved by integrating well-proven IT technologies and protocols into a package relevant in the emergency medicine field. The system has been reviewed by over 40 ambulance crews and is part of a newly co-designed ambulance treatment space.

Plasma diagnostics and ITO film deposition by RF-assisted closed-field dual magnetron system

Deposition of indium tin oxide (ITO) among various transparent conductive materials on flexible organic substrates has been intensively investigated among academics and industrials for a whole new array of imaginative optoelectronic products. One critical challenge coming with the organic materials is their poor thermal endurances, considering that the process currently used to produce industry-standard ITO usually involves relatively high substrate temperature in excess of 200°C and post-annealing. A lower processing temperature is thus demanded, among other desires of high deposition rate, large substrate area, good uniformity, and high quality of the deposited materials. For this purpose, we developed an RF-assisted closed-field dual magnetron sputtering system. The “prototype” system consists of a 3-inch unbalanced dual magnetron operated at a closed-field configuration. An RF coil was fabricated and placed between the two magnetron cathodes to initiate a secondary plasma. The concept is to increase the ionization faction with the RF enhancement and utilize the ion energy instead of thermal energy to facilitate the ITO film growth. The closed-field unbalanced magnetrons create a plasma in the intervening region rather than confine it near the target, thus achieving a large-area processing capability. An RF-compensated Langmuir probe was used to characterize and compare the plasmas in mirrored balanced and closed-field unbalanced magnetron configurations. The spatial distributions of the electron density ne and electron temperature Te were measured. The density profiles reflect the shapes of the plasma. Rather than intensively concentrated to the targets/cathodes in the balanced magnetrons, the plasma is more dispersive in the closed-field mode with a twice higher electron density in the substrate region. The RF assistance significantly enhances ne by one or two orders of magnitude higher. The effect of various other parameters, such as pressure, on the plasma was also studied. The ionization fractions of the sputtered atoms were measured using a gridded energy analyzer (GEA) combined with a quartz crystal microbalance (QCM). The presence of the RF plasma effectively increases the ITO ionization fraction to around 80% in both the balanced and closed-field unbalanced configurations. The ionization fraction also varies with pressure, maximizing at 5-10 mTorr. The study of the ionization not only facilitates understanding the plasma behaviors in the RF-assisted magnetron sputtering, but also provides a criterion for optimizing the film deposition process. ITO films were deposited on both glass and plastic (PET) substrates in the 3-inch RF-assisted closed-field magnetrons. The electrical resistivity and optical transmission transparency of the ITO films were measured. Appropriate RF assistance was shown to dramatically reduce the electrical resistivity. An ITO film with a resistivity of 1.2×10-3 Ω-cm and a visible light transmittance of 91% was obtained with a 225 W RF enhancement, while the substrate temperature was monitored as below 110°C. X-ray photoelectron spectroscopy (XPS) was employed to confirm the ITO film stoichiometry. The surface morphology of the ITO films and its effect on the film properties were studied using atomic force microscopy (AFM). The prototype of RF-assisted closed-field magnetron was further extended to a larger rectangular shaped dual magnetron in a flat panel display manufacturing system. Similar improvement of the ITO film conductivities by the auxiliary RF was observed on the large-area PET substrates. Meanwhile, significant deposition rates of 25-42 nm/min were achieved.

DYNAMIC MODEL BASED STATE ESTIMATION IN A HEAVY DUTY DIESEL AFTERTREATMENT SYSTEM FOR ONBOARD DIAGNOSTICS AND CONTROLS

Estimating un-measurable states is an important component for onboard diagnostics (OBD) and control strategy development in diesel exhaust aftertreatment systems. This research focuses on the development of an Extended Kalman Filter (EKF) based state estimator for two of the main components in a diesel engine aftertreatment system: the Diesel Oxidation Catalyst (DOC) and the Selective Catalytic Reduction (SCR) catalyst. One of the key areas of interest is the performance of these estimators when the catalyzed particulate filter (CPF) is being actively regenerated. In this study, model reduction techniques were developed and used to develop reduced order models from the 1D models used to simulate the DOC and SCR. As a result of order reduction, the number of states in the estimator is reduced from 12 to 1 per element for the DOC and 12 to 2 per element for the SCR. The reduced order models were simulated on the experimental data and compared to the high fidelity model and the experimental data. The results show that the effect of eliminating the heat transfer and mass transfer coefficients are not significant on the performance of the reduced order models. This is shown by an insignificant change in the kinetic parameters between the reduced order and 1D model for simulating the experimental data. An EKF based estimator to estimate the internal states of the DOC and SCR was developed. The DOC and SCR estimators were simulated on the experimental data to show that the estimator provides improved estimation of states compared to a reduced order model. The results showed that using the temperature measurement at the DOC outlet improved the estimates of the CO , NO , NO2 and HC concentrations from the DOC. The SCR estimator was used to evaluate the effect of NH3 and NOX sensors on state estimation quality. Three sensor combinations of NOX sensor only, NH3 sensor only and both NOX and NH3 sensors were evaluated. The NOX only configuration had the worst performance, the NH3 sensor only configuration was in the middle and both the NOX and NH3 sensor combination provided the best performance.

Project Diagnostics : Providing Successful Project Outcomes

Project Diagnostics is a tool for construction industry stakeholders wishing to improve project delivery and outcomes. This software identifies areas of poor project health, then establishes probable root causes and provides suggested remedial measures. Its focus is to act as an advanced warning system for construction projects that are failing to meet predetermined objectives based on the critical success factors (CSFs) of cost, time, quality, safety, relationships, environment and stakeholder value.