Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022S10211410

A stable and accurate control-volume technique based on integrated radial basis function networks for fluid-flow problems

Radial basis function networks (RBFNs) have been widely used in solving partial differential equations as they are able to provide fast convergence. Integrated RBFNs have the ability to avoid the problem of reduced convergence-rate caused by differentiation. This paper is concerned with the use of integrated RBFNs in the context of control-volume discretisations for the simulation of fluid-flow problems. Special attention is given to (i) the development of a stable high-order upwind scheme for the convection term and (ii) the development of a local high-order approximation scheme for the diffusion term. Benchmark problems including the lid-driven triangular-cavity flow are employed to validate the present technique. Accurate results at high values of the Reynolds number are obtained using relatively-coarse grids

Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Application of artificial intelligence techniques to the smart control of sheet metal forming processes

The present research work aims at evaluating the economical feasibility and the technological viability of implementing intelligent control systems in complex industrial manufacturing processes; in this case forming processes. Forming processes are manufacturing processes that use force and pressure in order to modify the shape of a material part until getting the final product. The wide range of non-linear factors (material properties, tool geometry, machine parameters and lubrication variables) that determine the final quality of the parts manufactured by these processes makes them to be inherently quite unstable. Thus, the control made by human operators is still essential nowadays. On the other hand, although human operators have demonstrated to be a very successful strategy when controlling this type of processes, the actual market evolution towards the fabrication of more complex parts, made of lower formability materials at higher production rates is decreasing their capacity of reaction when solving the daily problems. Therefore, the development of new automatic and global control systems based, not on traditional control techniques and mathematical models but on the control strategy that has been successfully used for many years, the control through the experience and knowledge, is now even more necessary. In the present research work, two intelligent control systems based on AI techniques have been developed and evaluated. The main purpose of these intelligent control systems is to identify the process failures at forming processes and to propose the right solutions that should lead to their solution, all this in a quick and reliable way. Following this strategy, the solution of the process failures is considerably simplified because, after any process failure of defective part detection, human operators find a report where an explanation of the incidence, as well as its causes and the way to solve it, are displayed. This has the inherent advantage of decreasing the length of the downtimes at the manufacturing facilities and thus increasing the number of parts produced. Together with the previously described core of the global control systems, two monitoring systems have been developed and implemented in a forming facility too. The purpose of these monitoring systems is to work as the senses of the intelligent control systems. The first one, an artificial vision system, is aimed at evaluating the quality of the produced parts by carrying out a 100% quality control at the end of the forming process. This will assure the right quality of all the products shipped to the customer. The second one, a sensors based process monitoring system, is aimed at detecting any process failure at the forming facility by means of force and acoustic emissions measurements. This will reduce the internal defective and will assure the security of the forming facility. Both systems are in charge of detecting any process failure and defective part and of reporting about them to the intelligent control system. Since the aim of the research work was to evaluate the feasibility of implementing global intelligent control systems in the industry, all the developments and results achieved through the present research work have been carried out in an industrial environment. The research work is principally divided into three main parts; 1) the development and implementation of the sensors based process monitoring system, 2) the development and implementation of the AV monitoring system and 3) the development of the intelligent control systems. At the end, a summary of all the results and conclusions achieved through the development of the previous mentioned systems is given too.Ikerkuntza lan honen helburua sistema adimendunak fabrikazio prozesu konplexuak kontrolatzeko erabiltzearen bideragarritasuna aztertzea da, bai ekonomikoki eta teknologikoki. Kasu honetan, konformazio prozesuetan inplementatutako sistema adimenduak ikertu dira. Konformazio prozesuak, amaierako produktua lortzeko, hasierako materialari esfortzu edo presioen bidez forma geometrikoa aldatzean datzate. Konformaturiko piezen amaierako kalitatea finkatzen duten aldagai ez-linealen ugaritasun zabalak (materialen propietateak, lanabesen geometriak, makinen parametroak eta/edo lubrifikazioa) prozesu hauek ezegonkorrak izatea ondorioztatzen du. Hori dela medio, gaur egun ere, prozesu hauen kontrola giza-langile bidez egiten da. Langileak prozesu hauek modu eraginkorrean kontrolatzeko gai direla erakutsi du esperientziak. Dena den, deformagarritasun txikiagoko materialez eginiko pieza konplexuagoak kadentzia altuagoetan fabrikatzeko gaur egungo joerak, langileek ezustekoen aurrean erantzuteko duten gaitasuna gutxitu du. Ondorioz, prozesua gainbegiratu eta kontrolatzen duten sistema automatiko eta adimendu berrien garapena beharrezkoa bihurtu da. Sistema hauek ez daude kontrol teknika tradizional edo eredu matematikoetan oinarrituak. Sistema hauen kontrola ezagutza eta esperientzian oinarriturik dago, zeinak azken urteetan emaitza onak eman dituen. Ikerkuntza lan honetan adimen artifizial tekniketan oinarrituriko bi kontrol sistema adimendun garatu eta baloratu dira. Sistema hauen helburu nagusia konformazio prozesuetan emaniko akatsak identifikatu eta automatikoki ebazpenproposamenak aurkeztea da, modu azkar eta sendoan. Estrategia hau jarraituz, prozesuko akatsen ebazpena errazten da, pieza akastunak atzematean edo makinaren geldialdi baten aurrean, sistemak langilea eman beharreko pausuak azaltzen dizkion txosten batez hornituko baitu. Makinaren geldialdiaren murriztea eta ondorioz, produktibitatea igotzea da honen abantaila nagusia, akatsen identifikazioa berehalakoa baita. Kontrol sistema garatzeaz gain, puntzonaketa instalakuntza batean bi monitorizazio sistema martxan jarri dira. Bi monitorizazio sistema hauen helburua prozesuaren informazioa jaso eta kontrol sistemari bidaltzea da. Lehenengoa ikuspen artifizialeko sistema bat da, zeinaren helburua ekoiztutako piezen %100aren kalitatea aztertzea den. Honenbestez, bezeroei bidalitako piezen kalitate egokia bermatzen da. Bigarrena sentsoreetan oinarrituriko prozesuen monitorizazio sistema bat da. Bere helburua prozesuan emaniko edozein akats antzematea da. Honek akastun piezen kantitatea gutxitzen du eta instalakuntzak prozesuen ezegonkortasunetatik babesten ditu. Ondorioz, bi sistemen helburua prozesuan izandako arazo edo pieza akastunak antzematea eta kontrol sistemari hauen berri ematea da. Lan honen helburua aurrez aipaturiko sistemen gaitasuna industri ingurunean ebaluatzea denez, aurkezturiko garapen eta emaitzak enpresa batean burutu dira. Hiru atal nagusi bereiz daitezke lan honetan: 1) sentsoreetan oinarrituriko monitorizazio sistema baten garapen eta inplementazioa; 2) ikuskapen artifizialeko sistemaren garapen eta inplementazioa; eta 3) adimendun kontrolean oinarrituriko sistemen garapena.El presente trabajo de investigación tiene como objetivo evaluar en qué condiciones es económicamente viable y tecnológicamente factible la implementación de sistemas inteligentes de control en procesos de fabricación complejos; en este caso procesos de conformado. Los procesos de conformado son procesos de fabricación basados en la aplicación de esfuerzos o presiones sobre componentes con el objetivo de modificar su forma geométrica hasta conseguir un producto final. El gran abanico de variables no lineales (propiedades de materiales, geometría de herramientas, parámetros de máquinas y/o lubricación) que determinan la calidad final de las piezas conformadas hacen que estos procesos sean inherentemente inestables. Por ello, aun hoy en día, el control de estos procesos se realiza mediante operarios humanos. Por otro lado, aunque la experiencia ha demostrado que los operarios son capaces de controlar estos procesos de manera eficiente, la actual tendencia hacia la fabricación de piezas más complejas, fabricadas en materiales menos deformables y todo ello a cadencias de fabricación mayores, ha hecho que la capacidad de los operarios para reaccionar ante imprevistos se haya visto mermada. Por lo tanto, el desarrollo de nuevos sistemas automáticos e inteligentes de supervisión y control basados, no en técnicas tradicionales de control o en modelos matemáticos, sino en la estrategia de control que ha dado buenos resultados a lo largo de los años, el control basado en la experiencia y el conocimiento, es cada vez más necesario. En el presente trabajo de investigación, se han desarrollado y evaluado dos sistemas inteligentes de control basados en técnicas de inteligencia artificial. El principal objetivo de estos sistemas es ser capaces de identificar los fallos de proceso en procesos de conformado así como de plantear, automáticamente, las instrucciones para su resolución, todo ello de una manera rápida y robusta. Siguiendo esta estrategia, la resolución de los fallos de proceso se simplifica ya que, tras una parada de máquina o la detección de piezas defectuosas, el sistema proporciona al operario un informe donde se detallan las acciones a llevar a cabo. Esto tiene como ventaja una reducción en los tiempos de parada de máquina (y por lo tanto aumento en la cantidad de piezas producidas) ya que la identificación de los fallos es inmediata. Junto con el núcleo del sistema global de control, se han desarrollado e implementando en una instalación de corte progresivo dos sistemas de monitorización. El objetivo de estos dos sistemas de monitorización es recoger información sobre el proceso y enviársela al sistema de control. El primero, un sistema de visión artificial, tiene como objetivo analizar la calidad del 100% de las piezas fabricadas. Esto asegura la correcta calidad de todas las piezas enviadas a los clientes. El segundo, un sistema de monitorización de procesos basado en sensores, tiene como objetivo la detección de cualquier fallo de proceso. Esto reduce el defectivo interno y protege a las instalaciones frente a anomalías de proceso. Por lo tanto, ambos sistemas tienen como misión la detección de cualquier anomalía de proceso o pieza defectiva así como informar al sistema de control sobre las mismas. Puesto que el objetivo de este trabajo es evaluar la capacidad de los sistemas anteriormente citados en el entorno industrial, todos los desarrollos y resultados obtenidos a lo largo del mismo se han llevado a cabo en una empresa. El trabajo se puede dividir en tres partes: 1) el desarrollo e implementación del sistema de monitorización basado en sensores, 2) el desarrollo e implementación del sistema de visión artificial y 3) el desarrollo de los sistemas de control inteligentes

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Applications of aerospace technology in the public sector

Current activities of the program to accelerate specific applications of space related technology in major public sector problem areas are summarized for the period 1 June 1971 through 30 November 1971. An overview of NASA technology, technology applications, and supporting activities are presented. Specific technology applications in biomedicine are reported including cancer detection, treatment and research; cardiovascular diseases, diagnosis, and treatment; medical instrumentation; kidney function disorders, treatment, and research; and rehabilitation medicine

An Efficient Quality-Related Fault Diagnosis Method for Real-Time Multimode Industrial Process

Focusing on quality-related complex industrial process performance monitoring, a novel multimode process monitoring method is proposed in this paper. Firstly, principal component space clustering is implemented under the guidance of quality variables. Through extraction of model tags, clustering information of original training data can be acquired. Secondly, according to multimode characteristics of process data, the monitoring model integrated Gaussian mixture model with total projection to latent structures is effective after building the covariance description form. The multimode total projection to latent structures (MTPLS) model is the foundation of problem solving about quality-related monitoring for multimode processes. Then, a comprehensive statistics index is defined which is based on the posterior probability of the monitored samples belonging to each Gaussian component in the Bayesian theory. After that, a combined index is constructed for process monitoring. Finally, motivated by the application of traditional contribution plot in fault diagnosis, a gradient contribution rate is applied for analyzing the variation of variable contribution rate along samples. Our method can ensure the implementation of online fault monitoring and diagnosis for multimode processes. Performances of the whole proposed scheme are verified in a real industrial, hot strip mill process (HSMP) compared with some existing methods

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

Rolling contact fatigue failures in silicon nitride and their detection

The project investigates the feasibility of using sensor-based detection and processing systems to provide a reliable means of monitoring rolling contact fatigue (RCF) wear failures of silicon nitride in hybrid bearings. To fulfil this investigation, a decision was made early in the project to perform a series of hybrid rolling wear tests using a twin disc machine modified for use on hybrid bearing elements.The initial part of the thesis reviews the current understanding of the general wear mechanisms and RCF with a specific focus to determine the appropriate methods for their detection in hybrid bearings. The study focusses on vibration, electrostatic and acoustic emission (AE) techniques and reviews their associated sensing technologies currently deployed with a view of adapting them for use in hybrids. To provide a basis for the adaptation, an understanding of the current sensor data enhancement and feature extraction methods is presented based on a literature review.The second part describes the test equipment, its modifications and instrumentation required to capture and process the vibration, electrostatic and AE signals generated in hybrid elements. These were identified in an initial feasibility test performed on a standard twin disc machine. After a detailed description of the resulting equipment, the thesis describes the calibration tests aimed to provide base data for the development of the signal processing methods.The development of the signal processing techniques is described in detail for each of the sensor types. Time synchronous averaging (TSA) technique is used to identify the location of the signal sources along the surfaces of the specimens and the signals are enhanced by additional filtering techniques.The next part of the thesis describes the main hybrid rolling wear tests; it details the selection of the run parameters and the samples seeded with surface cracks to cover a variety of situations, the method of execution of each test run, and the techniques to analyse the results.The research establishes that two RCF fault types are produced in the silicon nitride rolling element reflecting essentially different mechanisms in their distinct and separate development; i) cracks, progressing into depth and denoted in this study as C-/Ring crack Complex (CRC) and ii) Flaking, progressing primarily on the surface by spalls. Additionally and not reported in the literature, an advanced stage of the CRC fault type composed of multiple and extensive c-cracks is interpreted as the result of induced sliding in these runs. In general, having reached an advanced stage, both CRC and Flaking faults produce significant wear in the steel counterface through abrasion, plastic deformation or 3-body abrasion in at least three possible ways, all of which are described in details