Warranty Data Analysis: A Review

Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA

A general inspection and opportunistic replacement policy for one-component systems of variable quality

We model the influence of opportunities in a hybrid inspection and replacement policy. The base policy has two phases: an initial inspection phase in which the system is replaced if found defective; and a later wear-out phase that terminates with replacement and during which there is no inspection. The efficacy of inspection is modelled using the delay time concept. Onto this base model, we introduce events that arise at random and offer opportunities for cost-efficient replacement, and we investigate the efficacy of additional opportunistic replacements within the policy. Furthermore, replacements are considered to be heterogeneous and of variable quality. This is a natural policy for heterogeneous systems. Our analysis suggests that a policy extension that allows opportunities to be utilised offers benefit, in terms of cost-efficiency. This benefit is significant compared to those offered by age-based inspection or preventive replacement. In addition, opportunistic replacement may simplify maintenance planning

Aplicação de um modelo de riscos concorrentes na análise de confiabilidade de dados de garantia

Em análise de confiabilidade, espera-se que dados de vida de equipamentos sigam uma distribuição de probabilidade conhecida, como, por exemplo, uma distribuição de Weibull ou Lognormal. Entretanto, quando se modelam falhas originadas em campo, essas podem decorrer de causas múltiplas e os tempos-até-falha podem, assim, estar associados a diferentes estágios do ciclo de vida de um produto, não se ajustando a uma distribuição de probabilidade única. Neste artigo, propõe-se a utilização de um modelo misto que possa ser aplicado na análise de dados de vida oriundos de duas fases do ciclo de vida de um produto: a fase de vida operacional e a fase de envelhecimento (desgaste). O modelo proposto combina elementos de uma distribuição exponencial e de uma distribuição de Weibull com dois parâmetros. Equações de confiabilidade e estimadores de máxima verossimilhança são empregados para definir os parâmetros do modelo e para ilustrar os desenvolvimentos propostos. Um teste de ajuste é utilizado para verificar o desempenho do modelo sugerido

Delay-time modelling of a critical system subject to random inspections

We model the inspection-maintenance of a critical system in which the execution of inspections is random. The models we develop are interesting because they mimic realities in which production is prioritised over maintenance, so that inspections might be impeded or they might be opportunistic. Random maintenance has been modelled by others but there is little in the literature that relates to inspection of a critical system. We suppose that the critical system can be good, defective or failed, and that failure impacts on production, so that a failure is immediately revealed, but a defect does not. A defect, if revealed at inspection, is a trigger for replacement. We compare the cost and reliability of random inspections with scheduled periodic inspections and discuss the implications for practice. Our results indicate that inspections that are performed opportunistically rather than scheduled periodically may offer an economic advantage provided opportunities are sufficiently frequent and convenient. A hybrid inspection and replacement policy, with inspections subject to impediments, is robust to departure from its inspection schedule. Keywords: Maintenance; reliability; random inspection; production; qualit

Deep Learning Based Reliability Models For High Dimensional Data

The reliability estimation of products has crucial applications in various industries, particularly in current competitive markets, as it has high economic impacts. Hence, reliability analysis and failure prediction are receiving increasing attention. Reliability models based on lifetime data have been developed for different modern applications. These models are able to predict failure by incorporating the influence of covariates on time-to-failure. The covariates are factors that affect the subjects’ lifetime. Modern technologies generate covariates which can be utilized to improve failure time prediction. However, there are several challenges to incorporate the covariates into reliability models. First, the covariates generally are high dimensional and topologically complex. Second, the existing reliability models are not efficient in modeling the effect on the complex covariates on failure time. Third, failure time information may not be available for all covariates, as collecting such information is a costly and time-consuming process. To overcome the first challenge, we propose a statistical approach to model the complex data. The proposed model generalizes penalized logistic regression to capture the spatial properties of the data. An efficient parameter estimation method is developed to make the model practical in case of large sample sizes. To tackle the second challenge, a deep learning-based reliability model is proposed. The model can capture the complex effect of the data on failure time. A novel loss function based on the partial likelihood function is developed to train the deep learning model. Furthermore, to overcome the third difficulty, we proposed a transfer learning-based reliability model to estimate failure time based on the failure time of similar covariates. The proposed model is based on a two-level autoencoder to minimize the distribution distance of covariates. A new parameter estimation method is developed to estimate the parameter of the proposed two-level autoencoder model. Various simulation studies are conducted to demonstrate the proposed models. The results show that the proposed models outperformed the traditional statistical and reliability models. Moreover, physical experiments on advanced high strength steel are designed to demonstrate the proposed model. As microstructure images of the steels affect the failure time of the steel, the images are considered as covariates. The results show that the proposed models predict the failure time and hazard function of the materials more accurately than existing reliability models

Hazard rate models for early warranty issue detection using upstream supply chain information

This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier

Análisis estadístico de confiabilidad para equipos de elevación tipo Manlift (Plataforma de elevación para trabajo en alturas)

El presente documento ilustra la elaboración de un análisis estadístico de confiabilidad de acuerdo a los datos históricos de falla, para el equipo de elevación de personas Manlift Traccess 170, esto con el fin de determinar el estado actual de dichos activos y en base al análisis determinar acciones de mejora en la gestión de mantenimiento. Se inicia con la obtención de todos los datos correspondientes al activo, referente a las fallas reportadas en el software de mantenimiento, para luego ser ordenados, filtrados y seleccionados la información más útil para el estudio. Luego, se emplea tres distribuciones estadísticas (Normal, Weibull 2P y Log Normal) para describir el comportamiento de las fallas, determinando las funciones de confiabilidad adecuadas para el análisis. A través de pruebas de bondad de ajuste (Shi cuadrado y K-S) se selecciona el modelo más acorde a los datos reales, logrando predecir el comportamiento de las fallas. Después de obtenidos los resultados por el análisis se determina el impacto, la relevancia de los mismos y se traslada esta información a un análisis financiero que permite reflejar la utilidad del análisis de confiabilidad. Finalmente se obtienen conclusiones de ambos análisis realizados.This document illustrates the elaboration of a statistical reliability analysis according to the historical failure data, for the Manlift Traccess 170 people lifting equipment, this in order to determine the current status of said assets and based on the analysis determine improvement actions in maintenance management. It begins with obtaining all the data corresponding to the asset, referring to the failures reported in the maintenance software, and then the most useful information for the study is sorted, filtered and selected. Then, three statistical distributions (Normal, Weibull 2P and Log Normal) are used to describe the behavior of the faults, determining the appropriate reliability functions for the analysis. Through goodness-of-fit tests (Shi squared and K-S) the model that is most consistent with the real data is selected, being able to predict the behavior of the failures. After obtaining the results by the analysis, the impact and relevance of the same are determined and this information is transferred to a financial analysis that allows to reflect the usefulness of the reliability analysis. Finally, conclusions are obtained from both analyzes carried out.Enumeración de tablas Enumeración de figuras Resumen Abstract Introducción 1 Título de la investigación 2 Problema de investigación 2.1 Descripción del problema 2.2 Planteamiento del problema 2.3 Sistematización del problema 3 Objetivos de la investigación 3.1 Objetivo General 3.2 Objetivos Específicos 4 Justificación y delimitación 4.1 Justificación 4.2. Delimitación 4.3 Limitaciones 4 5 Marco conceptual 5.1 Estado del arte 5.1.1 Estado del arte Nacional 5.1.2 Estado del arte internacional 5.2 Marco Teórico 5.2.1 ¿Qué es un Manlift? 5.2.2 Traccess 170, equipo de elevación de personal (Manlift) 5.2.3 ¿Qué es la Ingeniería de Confiabilidad? 5.2.4 ¿Qué es el Análisis Estadístico? 5.2.5 ¿Qué son las Distribuciones continuas de probabilidad aplicadas a la confiabilidad? 5.2.6 ¿Qué es la distribución de Weibull? 5.2.7 ¿Qué es la Aproximación de Rangos medios5?2.8 ¿Qué Son Los Mínimos Cuadrados? 5.2.9 ¿Qué son las Pruebas de bondad de ajuste? 5.3 Marco normativo y legal 6. Diseño de la metodología y cronograma 6.1. Tipo de investigación 6.1.1. Fuentes de obtención de información 6.1.3 Metodología de la investigación 5 6.1.4. Recopilación de la información 7. Desarrollo del análisis estadístico 7.1 Obtención y organización de base de datos 7.1.1 Selección de datos 7.1.2 Limpieza de datos 7.2 Función inicial de densidad de probabilidad 7.2.1. Distribución Normal 7.2.2 Distribución de Weibull 2P 7.2.2.2 Regresión lineal 7.2.3 Distribución Log Normal 7.2.4 Pruebas de Bondad de Ajuste 7.2.5 Crecimiento de la confiabilidad – Método de Duane 7.3 Análisis financiero basado en resultado estadístico7.3.1. Datos clave 7.3.2. Organización de datos 7.3. Tabla de inversión vs costo de acuerdo a horómetro 7.4. Tabla de inversión vs costo de acuerdo a especialidad 108 7.5. Tabla de inversión vs costo de acuerdo a costo de mantenimiento total 8. Impactos alcanzados / esperados 9. Conclusiones 6 10. Recomendaciones. 118 BibliografíaEspecializaciónEspecialista en Gerencia de MantenimientoEspecialización en Gerencia de Mantenimient

A mixed-Weibull regression model for the analysis of automotive warranty data

Abstract: This paper presents a case study regarding the reliability analysis of some automotive components based on field failure warranty data. The components exhibit two different failure modes, namely early and wearout failures, and are mounted on different vehicles, which differ among themselves for car model and engine type, thus involving different operating conditions. Hence, the failure time of each component is a random variable with a bimodal pdf which also depends upon a vector of covariates that indexes the specific operating condition. Then, a mixed-Weibull distribution, where the pdf of each subpopulation (namely the ‘weak’ and ‘strong’ subpopulation) depends on the covariates through the scale parameter, is used to analyze the component lifetime. A Fortran algorithm for the maximum likelihood estimation of model parameters has been implemented and a stepwise procedure, in its backwards version, has been used to test the significance of covariates and to construct the regression model. The presence of a weak subpopulation has been verified and the fraction of weak units in the population has also been estimated. Finally, the adequacy of the proposed model to fit the observed data has been assessed

Optimal Burn-In under Complex Failure Processes: Some New Perspectives

Ph.DDOCTOR OF PHILOSOPH

Monetäre Bewertung des belastungsbasierten Leasings für Werkzeugmaschinen

Die Anschaffungskosten von Werkzeugmaschinen stellen eine finanzielle Herausforderung für Unternehmen dar. Das Leasing als alternative Finanzierung zum kreditfinanzierten Kauf verspricht Vorteile. Durch die Fortschritte der Digitalisierung können neuartige Pay-per-X Modelle angeboten werden, welche eine höhere Flexibilität der Zahlungen in volatilen Märkten versprechen. Die aktuellen Modelle sind jedoch dem Prinzipal-Agenten-Problem ausgesetzt. Der Leasingnehmer ist an einer hohen Auslastung der Maschine interessiert, der Leasinggeber an deren Werterhalt. Die Nutzung der Maschine ist dem Leasinggeber nicht bekannt und eine übermäßige Belastung führt zu erhöhtem Verschleiß und Wertverlust. Folglich wird dieses Restwertrisiko in die Leasingrate eingepreist. Das belastungsbasierte Leasing verspricht die Auflösung die-ses Problems, indem die durch den Leasingnehmer verursachte Belastung in die Leasingrate integriert wird. Inwiefern sich ein solches Modell für den Leasingnehmer lohnt, ist durch die ungewisse Abnutzung der Maschine aktuell nicht im Vorfeld bestimmbar. In der vorliegenden Dissertation wird die monetäre Bewertung eines entwickelten belastungsbasierten Leasings für Werkzeugmaschinen als Grundlage einer Investitionsentscheidung des Leasingnehmers untersucht. Dazu wird das belastungsbasierte Leasing konzeptionell und mathematisch auf Basis des Leasingvertrags mit Teilamortisation entwickelt. Der Amortisationsanteil der Leasingrate wird durch die Abnutzung der Werkzeugmaschine auf Baugruppenebene bestimmt. Durch die Verrechnung der Belastung verändern sich die Anreize und damit das Nutzungsverhalten des Leasingnehmers. Diese Veränderung führt zu einem höheren erwarteten Restwert und niedrigeren Lebenszykluskosten in Form der Instandhaltungskosten. Darauf aufbauend wird die Bewer-tungsmethode zur Investitionsentscheidung als computerausführbares Simulationsmodell entwickelt. Die Unsicherheit bezüglich der Maschinenbelastung und der Leasingraten ist hierbei ein zentrales Element. Die Unsicherheit wird durch eine Monte-Carlo-Simulation modelliert, wobei n-fache Grundmietzeiten simuliert und anhand des Kapitalwerts aller entscheidungsrelevanten Kosten in mehreren Szenarien bewertet werden. Die Entscheidung erfolgt zweistufig. Nach einer Vorauswahl der Investitionsalternativen anhand der stochastischen Dominanz der alternativen Risikoprofile, werden diese anhand einer Risikonutzenfunktionen nach dem μ-σ-Prinzip bewertet und die beste Alternative ausgewählt. Die unsichere Maschinenbelastung während der Grundmietzeit wird über ein Simulationsmodell auf Basis historischer Instandhaltungs- und Betriebsdaten beschrieben. Dieser Simulationskern besteht zum einen aus der Ereignissimulation auf Basis statistischer, parametrischer Zuverlässigkeitsmodelle. Zum anderen besteht er aus der Abnutzungssimulation durch einen Gamma-Prozess als Approximation der Belastung. Die veränderte Nutzung der Maschine wird durch Manipulation der Datenbasen beschrieben. Der Bewertungsansatz wird am Beispiel einer Investitionsentscheidung eines mittelständischen Unternehmens validiert. Es wird gezeigt, dass sowohl in Bezug auf die Leasingraten als auch die Lebenszykluskosten das belastungsbasierte dem klassischen Leasing vorzuziehen ist, da es zu geringeren erwarteten Kosten und einem höheren Maschinenrestwert führt. Das neue Modell besitzt durch das gesunkene Restwertrisiko und die gesteigerte erwartete Gewinnmarge auch Vorteile für den Leasinggeber gegenüber dem klassischen Leasing