Using statistical models in industrial equipment

Statistical methods are nowadays more and more useful in industrial engineering. From plant design reliability to equipment analysis, there is much to cover with statistical models in order to improve the efficiency of systems. At Sines refinery we used several approaches trying to relate several process variables with the vibration of critical equipment and to model time to failure using parametric and non-parametric models. Critical pumps are also a purpose of the study and we focus on a 2oo3 (two-out-of-three) structure in order to check if maintenance optimization or maintenance cost reduction is possible, and thus, it will be included the study of the operation of some rotary equipment

Modelos de fiabilidade em equipamentos mecânicos

Dissertação apresentada para obtenção do grau de Doutor em Engenharia Mecânica, na Faculdade de Engenharia da Universidade do Porto, sob a orientação dos Prof. Doutores Luís Andrade Ferreira e Armando Leitã

Modelos de fiabilidade em equipamentos mecanicos

Maintenance, when optimized, has become in recent years one of the most important functions contributing to profitability. As a significant component in the overall cost budget, maintenance optimization can lead to benefits in terms of important reductions in operating costs. Therefore any model that contributes to improving the prediction procedures of the equipment condition is a tool of the upmost importance. In fact, by predicting the state of equipment it is possible to forecast its ability to produce in conformity with quality norms and requirements. Reliability models are a tool serving Maintenance in the achievement of this aim. Among the range of options available, non parametric models enable us to avoid establishing at the outset defined hazard or reliability functions; within the non parametric range the proportional hazards model allows simultaneous evaluation of the various factors involved in failure modes. In this work the proportional hazard model is applied to two complex types of equipment for which maintenance costs assume significant values, a mineral mill and four grinding machines for automobile engine parts. The proportional hazard model, which implies that hazards are proportional to each other within each given group of co variables that affect the occurrence of failures, and the relative influence of each factor within the group, provides a forecast of the future reliability of the equipment to which it is applied. The Maintenance Manager is able, on the basis of this data, to define the optimal maintenance to carry out on the equipment during the period covered by the forecast. In the case of the four grinders, the proportional hazard model with time dependent co variates is applied to two machines, both producing the same type of component - valve rocker arms. A relation is established between the condition and quality co variates. In conclusion the advantages and disadvantages of the model, from the phase of defining co variates and failure modes through to results analysis are summarizedAvailable from Fundacao para a Ciencia e a Tecnologia, Servico de Informacao e Documentacao, Av. D. Carlos I, 126, 1249-074 Lisboa, Portugal / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga

USE OF SURVIVAL MODELS IN A REFINERY 

Statistical methods are nowadays increasingly useful in industrial engineering. From plant design reliability to equipment analysis, there is much to cover with statistical models in order to improve the efficiency of systems. At Sines refinery we found it useful to apply a Cox model to a particular critical equipment trying to find process variables that cause its vibration as well as to apply well known distributions to baseline hazard rate

Application of Mixture Models to Survival Data

Survival models are being widely applied to the engineering field to model time-to-event data once censored data is here a common issue. Using parametric models or not, for the case of heterogeneous data, they may not always represent a good fit. The present study relays on critical pumps survival data where traditional parametric regression might be improved in order to obtain better approaches. Considering censored data and using an empiric method to split the data into two subgroups to give the possibility to fit separated models to our censored data, we’ve mixture two distinct distributions according a mixture-models approach. We have concluded that it is a good method to fit data that does not fit to a usual parametric distribution and achieve reliable parameters. A constant cumulative hazard rate policy was used as well to check optimum inspection times using the obtained model from the mixture-model, which could be a plus when comparing with the actual maintenance policies to check whether changes should be introduced or not

Frailty Models for Survival Data applied to Maintenance Management

Due to large number of equipment in industrial companies, maintenance management may not always be able to identify and quantify assets heterogeneity concerning failure data. Several statistical and mathematical methods are being developed in the last years looking for an improvement on the system reliability. We’ve been working on survival analysis applied to equipment failure data using Cox proportional hazards, however, we notice that individualization with respect to the risk must be considered. Frailty models allowed us to identify equipment not following patterns of identical distributed times to failure. Although there exist some controversy around frailty models, claiming its poor contribution to the model, the truth is that when clustering our equipment by its id with a non-observed frailty component, we quickly identify those with higher influence on the hazard. This is particularly useful whether the intention is to find a homogeneous sample or work with all samples considering frailty component, contributing either way, to an enhanced and adaptive maintenance management