A hierarchical Bayesian regression framework for enabling online reliability estimation and condition-based maintenance through accelerated testing

Thanks to the advances in the Internet of Things (IoT), Condition-based Maintenance (CBM) has progressively become one of the most renowned strategies to mitigate the risk arising from failures. Within any CBM framework, non-linear correlation among data and variability of condition monitoring data sources are among the main reasons that lead to a complex estimation of Reliability Indicators (RIs). Indeed, most classic approaches fail to fully consider these aspects. This work presents a novel methodology that employs Accelerated Life Testing (ALT) as multiple sources of data to define the impact of relevant PVs on RIs, and subsequently, plan maintenance actions through an online reliability estimation. For this purpose, a Generalized Linear Model (GLM) is exploited to model the relationship between PVs and an RI, while a Hierarchical Bayesian Regression (HBR) is implemented to estimate the parameters of the GLM. The HBR can deal with the aforementioned uncertainties, allowing to get a better explanation of the correlation of PVs. We considered a numerical example that exploits five distinct operating conditions for ALT as a case study. The developed methodology provides asset managers a solid tool to estimate online reliability and plan maintenance actions as soon as a given condition is reached.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Ship Design, Production and Operation

Preclinical PET and MR Evaluation of 89Zr- and 68Ga-Labeled Nanodiamonds in Mice over Different Time Scales

Nanodiamonds (NDs) have high potential as a drug carrier and in combination with nitrogen vacancies (NV centers) for highly sensitive MR-imaging after hyperpolarization. However, little remains known about their physiological properties in vivo. PET imaging allows further evaluation due to its quantitative properties and high sensitivity. Thus, we aimed to create a preclinical platform for PET and MR evaluation of surface-modified NDs by radiolabeling with both short- and long-lived radiotracers. Serum albumin coated NDs, functionalized with PEG groups and the chelator deferoxamine, were labeled either with zirconium-89 or gallium-68. Their biodistribution was assessed in two different mouse strains. PET scans were performed at various time points up to 7 d after i.v. injection. Anatomical correlation was provided by additional MRI in a subset of animals. PET results were validated by ex vivo quantification of the excised organs using a gamma counter. Radiolabeled NDs accumulated rapidly in the liver and spleen with a slight increase over time, while rapid washout from the blood pool was observed. Significant differences between the investigated radionuclides were only observed for the spleen (1 h). In summary, we successfully created a preclinical PET and MR imaging platform for the evaluation of the biodistribution of NDs over different time scales

Predicting future of unattended machinery plants: A step toward reliable autonomous shipping

Future waterborne transport operations in short-sea, sea-river, and inland waterways can be performed by autonomous vessels. The automation of maritime shipping directly emphasizes reducing crew numbers, minimizing operational costs, and mitigating human error during the operation. Recent researches have focused on understanding autonomous navigation while the reliability of unattended machinery plant has received very little attention. This paper aims at developing a method for predicting the performance of failure-sensitive components that may be left unattended in autonomous shipping. The presented methodology adopts Bayesian Inference as the basis of the artificial intelligence for predicting maintenance schedules including repair, inspection, and irregular checks of unattended systems. A Multinomial Process Tree (MPT) is used to model the failures within the system, identify faulty components, and to predict their failure times. A real case study from a short sea voyage is adopted to demonstrate the application of the presented methodology. The results of this research will assist decision and policy-makers to prevent costly failures in Maritime Autonomous Surface Ships (MASS) and extend the service life of autonomous systems before any human intervention.</p

Arthropod Borne Diseases in Imposed War during 1980-88

Background: Personnel of military forces have close contact with natural habitat and usually encounter with bite of arthropods and prone to be infected with arthropod borne diseases. The imposed war against Iran was one of the most important and the longest war in the Middle East and even in the world and military people faced various diseases. The aim of this study was to review prevalence of arthropod borne diseases and to collect relevant information and valuable experiences during the imposed war. Methods: The present survey is a historical research and cross-sectional study, focused on arthropod fauna, situation of different arthropod borne diseases and also the ways which military personnel used to protect themselves against them. The information was adopted from valid military health files and also interviewing people who participated in the war. Results: Scabies, cutaneous leishmaniasis, sandfly fever and pediculosis were more prevalent among other arthropod -borne diseases in Iran-Iraq war. Measures to control arthropods and diseases at wartime mainly included: scheduled spraying of pesticides, leishmanization and treatment of patients. Conclusion: Although measures used during the war to control arthropods were proper, however, due to needs and importance of military forces to new equipment and technologies, it is recommended to use deltamethrin-impreg­nated bed net, permethrin treated military uniforms and various insect repellents in future

A hybrid type-2 fuzzy logic system and extreme learning machine for low-cost INS/GPS in high-speed vehicular navigation system

Due to the combined navigation system consisting of both Inertial Navigation System (INS) and Global Positioning System (GPS) in a complementary mode which assure a reliable, accurate, and continuous navigation system, we use a GPS/INS navigation system in our research. Because of the conditions of navigation system such as low-cost MEMS-based inertial sensors with considerable uncertainty in INS sensors, a highly noisy real data, and a long term outage of GPS signals during our flight tests, we enhance the positioning speed and accuracy by an Extreme Learning Machine (ELM) with the features of excellent generalization performance and fast learning speed. However, the generalization capability of ELM usually destabilizes with uncertainty existing in the dataset. In order to fix this limitation, first, a Type-2 Fuzzy Logic System (T2-FLS) handles the uncertainties in GPS/INS data, and then the final output ends up to the ELM to train and predict INS positioning error. We verify the efficiency of the suggested method in the estimation of speed and accuracy in INS sensors error during GPS satellites outage, particularly in real-time applications with a high-speed vehicle. Then, to evaluate the overall performance of the proposed method, the achieved results are discussed and compared to other methods like Extended Kalman Filter (EKF), wavelet-ELM, and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results present considerable achievement and open the door to the application of T2-FLS and ELM in GPS/INS integration even in severe conditions

Bayesian estimation for reliability engineering: Addressing the influence of prior choice

Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process

Reliability Estimation under Scarcity of Data: A Comparison of Three Approaches

During the last decades, the optimization of the maintenance plan in process plants has lured the attention of many researchers due to its vital role in assuring the safety of operations. Within the process of scheduling maintenance activities, one of the most significant challenges is estimating the reliability of the involved systems, especially in case of data scarcity. Overestimating the average time between two consecutive failures of an individual component could compromise safety, while an underestimate leads to an increase of operational costs. Thus, a reliable tool able to determine the parameters of failure modelling with high accuracy when few data are available would be welcome. For this purpose, this paper aims at comparing the implementation of three practical estimation frameworks in case of sparse data to point out the most efficient approach. Hierarchical Bayesian modelling (HBM), maximum likelihood estimation (MLE), and least square estimation (LSE) are applied on data generated by a simulated stochastic process of a natural gas regulating and metering station (NGRMS), which was adopted as a case of study. The results identify the Bayesian methodology as the most accurate for predicting the failure rate of the considered devices, especially for the equipment characterized by less data available. The outcomes of this research will assist maintenance engineers and asset managers in choosing the optimal approach to conduct reliability analysis either when sufficient data or limited data are observed

A Hybrid Multi-Criteria Decision Model (HMCDM) based on AHP and TOPSIS analysis to evaluate Maintenance Strategy

The aim of the present paper concerns a Hybrid Multi-Criteria Decision Model (HMCDM) to evaluate Maintenance Strategy. In order to improve production performance, in particular system availability and to reduce cost organization, in particular maintenance cost an integrated MCDM approach is proposed. The aim of the proposed method is to suggest the best maintenance solution for industrial systems. The new hybrid model is able to overcame the shortcomings of literature methods, matching Analytic Hierarchy Process (AHP) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for the evaluation of maintenance policy. The proposed model has been applied in a real case study in a water bottling company. Different maintenance alternatives were considered and different criteria and sub-criteria were evaluated using Reliability, Availability, Maintainability, Safety (RAMS) and production parameters. The outputs suggested the best maintenance solution for all machines in the analyzed company. The results highlight a Maintenance Cost reduction and a System Availability increase of analyzed water bottling company