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"Maintenance engineering"
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A rare failure detection model for aircraft predictive maintenance using a deep hybrid learning approach
The use of aircraft operation logs to develop a data-driven model to predict probable failures that could cause interruption poses many challenges and has yet to be fully explored. Given that aircraft is high-integrity assets, failures are exceedingly rare. Hence, the distribution of relevant log data containing prior signs will be heavily skewed towards the typical (healthy) scenario. Thus, this study presents a novel deep learning technique based on the auto-encoder and bidirectional gated recurrent unit networks to handle extremely rare failure predictions in aircraft predictive maintenance modelling. The auto-encoder is modified and trained to detect rare failures, and the result from the auto-encoder is fed into the convolutional bidirectional gated recurrent unit network to predict the next occurrence of failure. The proposed network architecture with the rescaled focal loss addresses the imbalance problem during model training. The effectiveness of the proposed method is evaluated using real-world test cases of log-based warning and failure messages obtained from the fleet database of aircraft central maintenance system records. The proposed model is compared to other similar deep learning approaches. The results indicated an 18% increase in precision, a 5% increase in recall, and a 10% increase in G-mean values. It also demonstrates reliability in anticipating rare failures within a predetermined, meaningful time frame.
Journal Article
A career as an operating and stationary engineer
\"We may not always notice them, but operating and stationary engineers help keep the world running smoothly. This practical resource explains the importance of operating and stationary engineers and provides descriptions for several jobs within each field, including surveyor, heavy equipment operator, boiler operator, HVACR engineer, and building manager. Readers will learn the educational requirements and job training that are necessary to obtain these jobs, as well as steps they can take right now to get them on the right path. Job outlook and information about trade unions and other resources are also provided.\"--Provided by publisher.
Book
A Contrast-Enhanced Feature Reconstruction for Fixed PTZ Camera-Based Crack Recognition in Expressways
Efficient and accurate recognition of highway pavement cracks is crucial for the timely maintenance and long-term use of expressways. Among the existing crack acquisition methods, human-based approaches are inefficient, whereas carrier-based automated methods are expensive. Additionally, both methods present challenges related to traffic obstruction and safety risks. To address these challenges, we propose a fixed pan-tilt-zoom (PTZ) vision-based highway pavement crack recognition workflow. Pavement cracks often exhibit complex textures with blurred boundaries, low contrast, and discontinuous pixels, leading to missed and false detection. To mitigate these issues, we introduce an algorithm named contrast-enhanced feature reconstruction (CEFR), which consists of three parts: comparison-based pixel transformation, nonlinear stretching, and generating a saliency map. CEFR is an image pre-processing algorithm that enhances crack edges and establishes uniform inner-crack characteristics, thereby increasing the contrast between cracks and the background. Extensive experiments demonstrate that CEFR improves recognition performance, yielding increases of 3.1% in F1-score, 2.6% in mAP@0.5, and 4.6% in mAP@0.5:0.95, compared with the dataset without CEFR. The effectiveness and generalisability of CEFR are validated across multiple models, datasets, and tasks, confirming its applicability for highway maintenance engineering.
Journal Article
SATA automotive paint from prep to final cost
\"Solvent-based paints have been used in automotive applications since the days when automobiles were called \"buggies\" and the horsepower was provided by, well, horses. But recent EPA regulations have restricted solvent-based paints for use only in approved professional paint booths, meaning that do-it-yourselfers can no longer use them--and it won't be long before their use is banned entirely. Paint manufacturers have raced to develop water-based paints as a replacement for the solvent-based paints previously used. These new water-based automotive paints are of very high quality, but they require different methods and techniques for proper application, virtually rendering previous automotive paint books obsolete. Automotive Paint from Prep to Final Coat is the first book to provide instruction in these new paints. In addition to this critical information, author and top professional painter JoAnn Bortles covers all the techniques necessary to get the great results your car deserves. From initial body-panel preparation, to troubleshooting common problems, to application of the final coat, and all steps in between, this book is the only reference you will need to ensure your DIY automotive paint job is done right the first time\"-- Provided by publisher.
Book
XGBoost based residual life prediction in the presence of human error in maintenance
Accurate maintenance decision making is essential for organizations like military and aviation. Immensely demanding situations like limited time availability for maintenance in strenuous conditions escalate the possibility of human errors in maintaining such equipment. Human errors in maintenance negatively impact the life of the systems. Human Reliability Analysis methodologies have evolved to systematically quantify the human error in terms of Human Error Probability. However, the exact effect of human error on every component’s life is unknown yet. In the presence of the diverse operating profiles for equipment, estimating such effects becomes a complex and mathematically challenging problem to be handled by conventional statistical techniques. This paper presents a machine learning approach to estimate the residual life of a component by incorporating the effect of human error in maintenance. Based on the nature of the maintenance data, a gradient boosting ensemble model (XGBoost) is developed, which predicts the residual life of the component while considering error induced by maintenance personnel during its maintenance. The model recommends the maintenance decision considering the predicted residual life and the user-defined future mission profile. Additionally, provision is made to capture the stochastic future operating profile. The developed model effectively handles the uncertainties and variabilities in expected future mission profiles and the correlation of multiple influencing parameters without increasing mathematical complexity. The developed model is illustrated in the decision making of replacement of a component in a mission-critical military system in pre-mission maintenance break. From the perspective of managerial implications, some of the key findings from numerical experiments on the developed model are presented.
Journal Article
Rigging equipment : maintenance and safety inspection manual
\"Safely maintain and operate rigging equipmentRigging Equipment: Maintenance and Safety Inspection Manual is a must-have for rigging contractors, facility managers, and equipment operators. Featuring regulations, standards, guidelines, and recommendations applicable to critical lifts, this practical guide provides maintenance and safety inspection checklists for rigging equipment, components, and systems, and addresses the required training, planning, and documentation. The safe rigging practices recommended in this book are framed in general terms to accommodate the many variations in rigging practices. Coverage includes: Operating rules--rigging hazards, OSHA regulations, consensus standards, and industry guidelines Operator qualifications, safe operating practices, and operating procedures Planning and preparation before performing rigging Lifting and hoisting equipment and rigging and scaffolding systems Ladders, stairways, ramps, hand and power tools, and electrical systems Maintenance schedules, care, and safe operation of equipment Inspection checklists for rigging equipment before, during, and after use Testing, certification, and registration of rigging equipment Preventive maintenance recordkeeping based on equipment manufacturer's recommendations Proper use of personal safety and protective equipment\"-- Provided by publisher.
Book
A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems
Accurate fault diagnosis and prognosis can significantly reduce maintenance costs, increase the safety and availability of engineering systems that have become increasingly complex. It has been observed that very limited researches have been reported on fault diagnosis where multi-component degradation are presented. This is essentially a challenging Complex Systems problem where features multiple components interacting simultaneously and nonlinearly with each other and its environment on multiple levels. Even the degradation of a single component can lead to a misidentification of the fault severity level. This paper introduces a new test rig to simulate the multi-component degradation of the aircraft fuel system. A machine learning-based data analytical approach based on the classification of clustering features from both time and frequency domains is proposed. The scope of this framework is the identification of the location and severity of not only the system fault but also the multi-component degradation. The results illustrate that (a) the fault can be detected with accuracy > 99%; (b) the severity of fault can be identified with an accuracy of almost 100%; (c) the degradation level can be successfully identified with the
R
-square value > 0.9.
Journal Article
Weibull recurrent neural networks for failure prognosis using histogram data
Weibull time-to-event recurrent neural networks (WTTE-RNN) is a simple and versatile prognosis algorithm that works by optimising a Weibull survival function using a recurrent neural network. It offers the combined benefits of the sequential nature of the recurrent neural network, and the ability of the Weibull loss function to incorporate censored data. The goal of this paper is to present the first industrial use case of WTTE-RNN for prognosis. Prognosis of turbocharger conditions in a fleet of heavy-duty trucks is presented here, where the condition data used in the case study were recorded as a time series of sparsely sampled histograms. The experiments include comparison of the prediction models trained using data from the entire fleet of trucks vs data from clustered sub-fleets, where it is concluded that clustering is only beneficial as long as the training dataset is large enough for the model to not overfit. Moreover, the censored data from assets that did not fail are also shown to be incorporated while optimising the Weibull loss function and improve prediction performance. Overall, this paper concludes that WTTE-RNN-based failure predictions enable predictive maintenance policies, which are enhanced by identifying the sub-fleets of similar trucks.
Journal Article