Integrated Human-AI Forecasting for Preventive Maintenance Task Duration Estimation

PurposeThis study focuses on the application of reliability-centered maintenance (RCM) to a textile industry steam boiler. The study aims to demonstrate the development and application of RCM to a steam boiler used in the textile industry.Design/methodology/approachRCM is a structured process that develops maintenance activities needed on physical resources in their operational environment to realize their inherent reliability by logically incorporating an appropriate mixture of reactive, preventive, condition-based and proactive maintenance methods. A detailed analysis of the RCM approach is presented to develop preventive maintenance (PM) program and improve the reliability and availability of the steam boiler system.FindingsThe research reveals that the identification of PM tasks is a good indicator of the PM program's efficiency and can serve as an important maintenance-related downtime source. It is also discovered that the majority of maintenance programs that claim to be proactive are, in fact, reactive. This article also shows how RCM may be successfully implemented to any system, resulting in increased system reliability.Research limitations/implicationsThe paper focuses on a pilot study of the development and implementation of the RCM technique to a textile industry steam boiler. It is suggested that the developed RCM model can be applied to the entire plant.Originality/valueThe paper presents a comprehensive RCM model framework as well as an RCM decision framework, providing maintenance managers and engineers with a step-by-step approach to RCM implementation. The proposed framework is significant in that it may be utilized for both qualitative and quantitative analysis at the same time.

Design and implementation of a maintenance planning and control system

Application of RCM for safety considerations in a steel plant

Energy efficiency improvement and timely preventive maintenance (PM) are critical in manufacturing industry due to the rising energy cost, environmental concerns, and increasing requirements on system reliability. By strategically turning appropriate machines in down state, the corresponding energy consumption can be reduced, and at the meantime, the necessary PM works can be carried out to increase PM completion rate and reduce potential extra expense on PM during nonproduction shifts. However, there is usually a tradeoff between time dedicated to production and time available for energy saving and PM. In this paper, a systematic method is developed to identify opportunity windows (OWs) during which certain machines can be shut down to save energy and PM tasks can be performed while maintaining a desired production throughput. The method is based on stochastic serial production lines and real-time production data. A profit function is formulated to illustrate the tradeoff between energy cost savings and potential throughput loss. The profit function is used to justify the cost savings by utilizing the proposed OWs during production operation.

Equipment maintenance is momentous for improving production efficiency, how to integrate maintenance into production to address uncertain problems has attracted considerable attention. This paper addresses a novel approach for integrating preventive maintenance (PM) into production planning of a complex manufacturing system based on availability and cost. The proposed approach relies on two phases: firstly, this study predicts required capacity of each machine through extreme learning machine algorithm. Based on analyzing historical data, the opportunistic periods are calculated for implementing PM tasks to have less impact on production and obtain the PM interval and duration. Secondly, this study obtains the scheduling planning and the least number of maintenance personnel through an improved ant colony optimization algorithm. Finally, the feasibility and benefits of this approach are investigated based on empirical study by using historical data from real manufacturing execution system and equipment maintenance system. Experimental results demonstrate the effectiveness of proposed approach, reduce personnel number while guarantee the maintenance tasks. Therefore, the proposed approach is beneficial to improve the company’s production efficiency.

Duration estimation is known to be far from veridical and to differ for sensory estimates and motor reproduction. To investigate how these differential estimates are integrated for estimating or reproducing a duration and to examine sensorimotor biases in duration comparison and reproduction tasks, we compared estimation biases and variances among three different duration estimation tasks: perceptual comparison, motor reproduction, and auditory reproduction (i.e. a combined perceptual-motor task). We found consistent overestimation in both motor and perceptual-motor auditory reproduction tasks, and the least overestimation in the comparison task. More interestingly, compared to pure motor reproduction, the overestimation bias was reduced in the auditory reproduction task, due to the additional reproduced auditory signal. We further manipulated the signal-to-noise ratio (SNR) in the feedback/comparison tones to examine the changes in estimation biases and variances. Considering perceptual and motor biases as two independent components, we applied the reliability-based model, which successfully predicted the biases in auditory reproduction. Our findings thus provide behavioral evidence of how the brain combines motor and perceptual information together to reduce duration estimation biases and improve estimation reliability.

8 - Implementation—Carrying RCM to the Floor

Maintenance study on WCLL-TBS ancillary systems in process room

Maintenance is performed to restore a system's inherent reliability. However, reliability is not enough: a maintenance task must “pay for itself‘ in dollars or readiness. This applies to all types of maintenance: corrective, preventive, and alterative. We can reduce corrective maintenance costs through preventive and alterative maintenance. We can reduce preventive maintenance costs by eliminating inapplicable and ineffecitve tasks. We must weigh alterative maintenance costs against the value of improved readiness and cost‐avoidance of future corrective maintenance.The U. S. Navy is improving alterative maintenance by engineering for reduced maintenance (ERM). This process corrects the root cause of some high‐cost items by replacing the system, component, or coating with an improved design or material. It overcomes the limit of preventive maintenance: higher levels of inherent reliability require design improvement.The U. S. Navy is improving preventive maintenance through condition‐based maintenance (CBM). The Navy's CBM initiative encompasses three parallel efforts: rules (improving maintenance requirements and plans), tools (using computer and diagnostic technology to facilitate maintenance decision‐making), and schools (educating all levels of maintenance decision‐makers in reliability and condition‐based maintenance engineering principles).This paper addresses how the U. S. Navy surface ship community plans to avoid inapplicable and ineffective maintenance and reduce maintenance costs without sacrificing reliability.

This paper shows the application of Discrete Event Simulation to study the effect of proposed maintenance strategies to the availability. The simulation study was conducted on a Surface Mount Technology (SMT) line. The model is built in the Discrete Event Simulation software WITNESS 14 to imitate this selected case study line to test out two proposed maintenance strategies. The first proposed strategy consists of Corrective Maintenance (CM) task only whereas the second proposed strategy is the introduction of Preventive Maintenance (PM) to the line. The effect of the two cases to the availability of each machine and system availability were verified with the simulation model to determine the most optimal solution. Although the simulation testing did not show improvement in availability for all station after the introduction of PM, the increment in system availability indicate that PM is the most optimal maintenance strategy for the SMT line.

La presente investigación de corte exploratorio, tiene a bien sensibilizar a la comunidad, científica, estudiantil y empresarial, entorno a las muchas bondades a nivel de reducción de costes operativos, que la articulación de las herramientas del Total Productive Maintenance (TPM) (a nivel de las prácticas de mantenimiento preventivo y correctivo), junto con las practicas universales del Project Management Institute (PMI), tiene a bien gestar en todas las organizaciones que les apliquen, independientemente de los entornos donde se implementen. Mediante la revisión de la evidencia empírica y teórica de estas.A lo largo, de este análisis, se escudriño un sinnúmero de elementos que documentan la potencialidad de las herramientas del TPM y PMI, las cuales pueden ser aplicadas por cualquier unidad de gestión empresarial, independientemente del rubro, procesos o funciones que esta desarrolle.Por otro lado, es imperativo mencionar, que tras recorrer los insumos teóricos y evaluar varios casos de aplicación del mantenimiento preventivo y correctivo, a lo largo del contexto latinoamericano, es importante el denotar que el mantenimiento preventivo y correctivo tiene la potencialidad de reducir los costos de operación de manera significativa.

An important component in critical chain project management is a scheduling methodology to support resource constraints. Given task duration estimates and precedence relations along with resource designation for each task, one seeks to adhere to a target due date as well as to shorten the estimated makespan. Key are two types of time buffers to absorb potential delays accumulated from preceding tasks, and this makes the problem complex and hard to solve. The “good enough” strategy has been prevalent in research, focusing on the development of heuristic solution methods. There has been only one attempt to minimize estimated makespan, wherein discrete algebra is utilized. The method is, however, inflexible as you must compose a search space. Even the slightest extension requires a major recast of the approach. Hence, we develop a new approach to find an exact optimal solution in an efficient manner. Given a project instance, an optimization problem is constructed as a mixed-integer linear-program, for which an exact solution can be found using an off-the-shelf solver. While the problem is NP hard, larger-scale project instances, including up to 30 tasks, can be solved within a practical time standard. Extension with respect to resource assignment is easily accomplished. Experimental results highlight the significance of the developed framework.

Tugboat is the supporting system that plays an essential role in port. A tugboat used for maneuvering, primarily pulling or pushing bigger vessel in open seas or river to be able to lean in port. PT.X known as a company has a prominent business field in provides such as tugboat. On the PT. X Annual Report of 2018 reported several cases that caused that related to the reliability of the main engine. The failure occurred on the main engine, affected commission days targeted was not achieved. Reliability-Centered Maintenance (RCM) is a maintenance method that focused on elevating the reliability of a component in the system(s). RCM used a risk management principle to determine the maintenance task and schedule appropriately. RCM process implemented using the Guide for Survey Based on Reliability-Centered Maintenance (RCM) by the American Burau of Shipping (ABS). Based on the results of this research, there were 38 task list type which is obtained based on the analysis of maintenance task allocation and planning. The percentage of maintenance types from each failure mode (task type) Preventive Maintenance (PM) is 29%, Condition Monitoring (CM) is 29%, Finding Failure (FF) is 21% and One-Time Change (OTC) is 21%. Work package for each interval is created from every failure mode for each interval for maintenance/inspection.

Multi-criteria model to support the definition of opportunistic maintenance policy: A study in a cogeneration system

Rolling contact fatigue (RCF) is a common cause of rail failure due to repeated stresses at the wheel-rail contact. This phenomenon is a real problem that greatly affects the safety of train operation. Preventive and corrective maintenance tasks have a big impact on the Life Cycle Cost (LCC) of railway assets, and therefore cutting-edge strategies based on predictive functionalities are needed to reduce it. A methodology based on physical models is proposed to predict the degradation of railway tracks due to RCF. This work merges a crack initiation and a crack growth model along with a fully nonlinear multibody model. From a multibody assessment of the vehicle-track interaction, an energy dissipation method is used to identify points where cracks are expected to appear. At these points, crack propagation is calculated considering the contact conditions as a function of crack depth. The proposed methodology has been validated with field measurements, conducted using Eddy Currents provided by the infrastructure manager Network Rail. Validation results show that RCF behavior can be predicted for track sections with different characteristics without the necessity of previous on-track measurements.