Estimation of Mean Time between Failures of a Two Tandem Mill of a Sugar Plant

When minutes of down-time can negatively impact the bottom line of a business, it is crucial that the physical infrastructure supporting be reliable. The equipment reliability can be achieved with a solid understanding of mean time between failures. Mean time between failures (MTBF) has been used for years as a basis for various maintenance decisions supported by various methods and procedures for lifecycle predictions. To quantifying a maintainable system or reliability we can use MTBF. For developing the mean time between failures model we can use make use of Poisson distribution, Weibull model and Bayesian model. In this paper we will be talking about complexities and misconceptions of MTBF and clarify criteria that need to be consider in estimating MTBF in a sequential manner. This paper sheds light on MTBF using examples throughout in an effort to simplify complexity. Keywords: MTBF, Two Tandem Mill, Sugar Mill, Reliability, Maintenance

Maintenance and sustainability: a systematic review of modeling-based literature

PurposeMaintenance is a critical business function with a great impact on economic, environmental and social aspects. However, maintenance decisions' planning has been driven by merely economic and technical measures with inadequate consideration of environmental and social dimensions. This paper presents a review of the literature pertaining to sustainable maintenance decision-making models supported by a bibliometric analysis that seeks to establish the evolution of this research over time and identify the main research clusters.Design/methodology/approachA systematic literature review, supported with a bibliometric and network analysis, of the extant studies is conducted. The relevant literature is categorized based on which sustainability pillar, or possibly multiple ones, is being considered with further classification outlining the application area, modeling approach and the specific peculiarities characterizing each area.FindingsThe review revealed that maintenance and sustainability modeling is an emerging area of research that has intensified in the last few years. This fertile area can be developed further in several directions. In particular, there is room for devising models that are implementable, based on reliable and timely data with proven tangible practical results. While the environmental aspect has been considered, there is a clear scarcity of works addressing the social dimension. One of the identified barriers to developing applicable models is the lack of the required, accurate and timely data.Originality/valueThis work contributes to the maintenance and sustainability modeling research area, provides insights not previously addressed and highlights several avenues for future research. To the best of the authors' knowledge, this is the first review that looks at the integration of sustainability issues in maintenance modeling and optimization.

Online Maintenance Prioritization via Monte Carlo Tree Search and Case-based Reasoning

When maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. We formulate this problem as an online prioritization problem using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, we use Case-based Reasoning (CBR) to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. We demonstrate that our proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the time needed to identify optimal maintenance actions as more experience is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly.

Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA’s open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs.

Flares after hydroxychloroquine reduction or discontinuation: results from the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort

ObjectivesTo evaluate systemic lupus erythematosus (SLE) flares following hydroxychloroquine (HCQ) reduction or discontinuation versus HCQ maintenance.MethodsWe analysed prospective data from the Systemic Lupus International Collaborating Clinics (SLICC) cohort, enrolled from 33 sites within 15 months of SLE diagnosis and followed annually (1999–2019). We evaluated person-time contributed while on the initial HCQ dose (‘maintenance’), comparing this with person-time contributed after a first dose reduction, and after a first HCQ discontinuation. We estimated time to first flare, defined as either subsequent need for therapy augmentation, increase of ≥4 points in the SLE Disease Activity Index-2000, or hospitalisation for SLE. We estimated adjusted HRs (aHRs) with 95% CIs associated with reducing/discontinuing HCQ (vs maintenance). We also conducted separate multivariable hazard regressions in each HCQ subcohort to identify factors associated with flare.ResultsWe studied 1460 (90% female) patients initiating HCQ. aHRs for first SLE flare were 1.20 (95% CI 1.04 to 1.38) and 1.56 (95% CI 1.31 to 1.86) for the HCQ reduction and discontinuation groups, respectively, versus HCQ maintenance. Patients with low educational level were at particular risk of flaring after HCQ discontinuation (aHR 1.43, 95% CI 1.09 to 1.87). Prednisone use at time-zero was associated with over 1.5-fold increase in flare risk in all HCQ subcohorts.ConclusionsSLE flare risk was higher after HCQ taper/discontinuation versus HCQ maintenance. Decisions to maintain, reduce or stop HCQ may affect specific subgroups differently, including those on prednisone and/or with low education. Further study of special groups (eg, seniors) may be helpful.

A Novel Classification Model SA-MPCNN for Power Equipment Defect Text

The text classification of power equipment defect is of great significance to equipment health condition evaluation and power equipment maintenance decisions. Most of the existing classification methods do not sufficiently consider the semantic relation between words in the same sentence and cannot extract deep semantic features. To tackle those problems, this article proposes a novel classification method by combining the self-attention mechanism and multi-channel pyramid convolution neural networks. We utilize the bidirectional gated recurrent unit to model the text sequence and, on this basis, improve self-attention layer to dot multiplication on the forward and backward features to obtain the global attention score. Thereby, effective features are enhanced, invalid features are weakened, and important text representation vectors are obtained. To solve the problem that the shallow network structure cannot extract deep semantic features, we design a multi-channel pyramid convolution network, which first extracts deep text features from the channels of different windows and then fuses the text features of each channel. By comparing with the state-of-the-art methods, the model in this article has better performance in text classification of power equipment defects.

Off-line Deep Reinforcement Learning for Maintenance Optimization

Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to the remaining useful estimation or the likelihood of failure in the near future help operators to assess the operating conditions and, therefore, making better repair and maintenance decisions. However, many operators believe remaining useful life estimation and failure prediction solutions are incomplete answers to the maintenance challenge. They would argue that knowing the likelihood of failure in a given time interval or having access to an estimation of the remaining useful life are not enough to make maintenance decisions which minimize the cost while keeping them safe. In this paper, we present a maintenance framework based on off-line deep reinforcement learning which instead of providing information such as likelihood of failure, suggests actions such as “continue the operation” or “visit a repair shop” to the operators in order to maximize the overall profit. Using off-line reinforcement learning makes it possible to learn the optimum maintenance policy from historical data without relying on expensive simulators. We demonstrate the application of our solution in a case study using NASA C-MAPSS dataset.

Aircraft system-level diagnosis with emphasis on maintenance decisions

This paper proposes a diagnostic technique that can predict component degradation for a number of complex systems. It improves and clarifies the capabilities of a previously proposed diagnostic approach, by identifying the degradation severity of the examined components, and uses a 3D Principal Component Analysis approach to provide an explanation for the observed diagnostic accuracy. The diagnostic results are then used, in a systematic way, to influence maintenance decisions. Having been developed for the Auxiliary Power Unit (APU), the flexibility and power of the diagnostic methodology is shown by applying it to a completely new system, the Environmental Control System (ECS). A major conclusion of this work is that the proposed diagnostic approach is able to correctly predict the health state of two aircraft systems, and potentially many more, even in cases where different fault combinations result in similar fault patterns. Based on the engineering simulation approach verified here, a diagnostic methodology suitable from aircraft conception to retirement is proposed.

Aircraft operators maintenance decisions supporting method

A key element of exploitation processes constitutes maintenance operations and tasks. While being conducted in the proper way, they have a crucial effect on achieving the assumed by aircraft designer and operator goals. Properly conducted maintenance operations allow to meet all the technical objects readiness requirements as well as to achieve desired acceptable risk level. Maintenance system effectiveness might be generally a crucial task for company or entity responsible for the maintenance. In this context, particularly relevant become technical object maintenance procedures and tasks developed by their manufacturers. Experience of the article authors quite early shows the need of the mainte-nance programmes modification. Aircraft manufacturers usually are not so eager to develop and implement mainte-nance programme modifications. Presented situation is very much the case in aviation transport. This was the reason why authors of this article decided to prepare and develop this elaboration which might constitute the assistance and supports complex technical objects users in maintenance decision. The main purpose of this article is to present maintenance decisions’ supporting method for the aircraft operators. This article provides guidelines which include a description of risk in the context of aviation maintenance and introduction of some methodologies, tools and criteria that support identification, analysis and evaluation of risk. Authors included idea, how the aircraft preventive maintenance could be used to mitigate aircraft failure risk during flight operations. It also shows how to adopt and develop effective maintenance program using tools for adequate risk analysis, optimal interval assignments, and selection of the most effective maintenance task. Authors presented methodology and de-scribed steps of the logic diagram analysis for the aircraft systems and their components, in order to manage and adopt aircraft maintenance program to fulfil aircraft airworthiness requirements and operational availability. The whole methodology was described on the basis of the F 16 aircraft maintenance system and with reference to the maintenance data. This article might also constitute an introduction to the aircraft maintenance programme development method.