Papers by Keyword: Remaining Useful Life

Abstract: Accurate prediction of tool Remaining Useful Life (RUL) is essential for reliable and cost-effective milling, particularly when machining commercially pure titanium (CP-Ti), where tool wear is highly irregular. In industrial practice, continuously varying cutting conditions further complicate tool condition monitoring and life prediction. This paper proposes a vibration-based monitoring framework for RUL prediction under strongly variable milling conditions. A hybrid deep learning model based on CNN–BiLSTM is developed to capture the non-stationary relationship between vibration signals and tool degradation. Performance is compared between a spindle-mounted, non-invasive sensor and a tri-axial accelerometer mounted on the machine table, and the benefit of sensor fusion is assessed. Results show that spindle vibration correlates strongly with tool degradation and achieves predictive performance close to that of multi-sensor configurations, while requiring minimal instrumentation. The proposed approach remains robust under variations in both operating conditions and wear mechanisms, enabling reliable RUL estimation in non-stationary milling environments.

Abstract: Predicting the Remaining Useful Life (RUL) of machinery and critical components is crucial for proactive maintenance and operational efficiency in industrial settings. This paper presents an approach to RUL prediction using the AdaBoost algorithm, a technique that iteratively improves prediction accuracy by focusing on difficult-to-predict cases. The AdaBoost algorithm will be extended to handle both binary and multi-class classification, enabling it to distinguish between various stages of degradation. By providing more granular insights into the health status of components, this approach enhances maintenance planning by allowing for more targeted, condition-based interventions. Early detection of varying levels of wear allows maintenance teams to schedule repairs or part replacements precisely when needed, reducing unplanned downtime and optimizing resource allocation. This study demonstrates the adaptability of AdaBoost in handling complex RUL prediction scenarios, thus supporting a more effective and data-driven approach to predictive maintenance in industrial applications.

Abstract: The ship hull construction suffers a decrease in strength performance over its life cycle due to corrosion and fatigue. Therefore, the risk of structural failure also rises during the extended service life if maintenance is not performed properly. The budget, on the other hand, limits these activities. As a result, it is critical for ship owners to plan an optimal maintenance program. The idea of this research is to find the best way to keep the hull's structural integrity due to corrosion. A time dependent corrosion model has been developed for failure prediction purposes, based on the historical data of plate thickness reduction. Failure scenarios are carried out on local, global and fatigue strength. This research adopted a semi-quantitative risk assessment along with reliability analysis to give strategic maintenance planning by lowering the risks that would be encountered. Hence, ensuring uninterrupted service of the ship throughout the service life. Finally, this study will be very useful as reference to establish risk informed program to evaluate the risk level of components of hull that guides to adjust inspection intervals without avoiding safety requirements.

Abstract: Ball bearings are critical components of any industrial rotary equipment. They constitute about 90% of industrial machines’ components – and are thus responsible for the largest proportion of failures – approximately 70-85% of downtime. Defected bearings, while in service, give rise to high vibration amplitudes in rotary equipment, resulting in great reduction in their operational efficiency coupled with high energy consumption. Their premature and inadvertent failure could result in unplanned equipment downtown – thereby causing production loss and increased maintenance cost. Patently, to curtail this, it is vital that their health state is monitored throughout their service life for early faults detection, diagnosis, and prognosis. A knowledge of when a bearing will fail – that is, its remaining useful life (RUL) – can serve as supplement to maintenace decision-making such as determining in advance the time an equipment needs to be taken out-of-service and that can alternatively allow for sufficient lead time for maintenance planning as well. This can correspondingly result in enhancement in rotary systems effectiveness – i.e., availability, reliability, maintainability, and capability. Three popular condition monitoring approaches are signal processing-based approaches namely fault size estimation (FSE) and fault degradation estimation (FDE) as well as artifial intelligent (AI) based approach. It is, however, still a challenge to estimate a bearing fault size and therefore its RUL with high precision based on what has been diagnosed using these approaches. Accordingly, this review holistically explore capabilities and limitations of these approaches from recently published work. The reviewed limations are summarized and serve as new research avenue.

Abstract: Common 13Cr steel is a kind of corrosion resisting material to be used as gas well pipe working under the situation of high pressure, high temperature and high H₂S, but it’s still destroyed in an undesirable period because of the poor working environment. To forecast its remaining useful life, parameters indicating the failure of gas well pipe with high pressure and high temperature and high H₂S are collected to form a dataset. The dataset is then transformed into fuzzy set and gas wells are classified according to it. Then the failure time forecast model of gas well pipe is established based on the well’s fuzzy set. Finally, 2 wells remaining life is calculated by the model. It is verified that the result got is in line with the actual situation. The model established is importantly meaningful for the safety production and the proper selection of gas well pipe.

Abstract: Remaining useful life (RUL) is of great importance to energy supply systems, such as battery pack. In this paper, an optimization model of RUL for the paralleled battery pack on expansion mode is proposed based on the basic concept of RUL and the capacity fading model. genetic algorithm (GA) is adopted to solve the problem. Case study shows that the battery pack's RUL can be extended by using the optimization model and GA.

Abstract: This paper proposed a remaining useful life prediction model to avoid the original monitoring information due to the influence of the oil monitoring linear regression process, thereby reducing the prediction error. According to the process of equipment wear, we analyzed the impact of the relationship between the wear, the metal particle concentration and the remaining useful life; then established an improved filter model. Using maximum likelihood parameter to estimate model parameters. Finally, taking a certain type of self-propelled Gun Engine Oil Spectrum Data for example, and the results show that the remaining useful life prediction model of equipment has a certain practical value.

Abstract: As one of the most widely used energy storage systems, lithium-ion batteries are attracting more and more attention, and the estimation of lithium-ion batteries remaining useful life (RUL) becoming a critical problem. Generally, RUL can be predicted in two ways: physics of failure (PoF) method and data driven method. Due to the internal electro-chemical reactions are either inaccessible to sensors or hard to measure; the data-driven method is adopted because it does not require specific knowledge of material properties. In this paper, three data-driven algorithms, i.e., Support Vector Machine (SVM), Autoregressive Moving Average (ARMA), and Particle Filtering (PF) are presented for RUL prediction. The lithium-ion battery aging experiment data set has been trained to implement simulation. Based on the RUL prediction result, we can conclude that: (1) ARMA model achieved better result than SVM, however, the result shows a linear trend, which fail to properly reflect the degradation trend of the battery; (2) SVM often suffers from over fitting problem and is more suitable for single-step prediction; and (3) PF approach achieved a better prediction and reflected the trends of degradation of the battery owing to its combined with specific model.

Abstract: Due to compressor fouling, gas turbine efficiency decreases over time, resulting in decreased power output of the plant. To counteract the effects of compressor fouling, compressor on-line and off-line washing procedures are used. The present research is aimed to propose a method of mathematical modeling of offline washing interval which will be estimated as the RUL of compressor based on Proportional hazards model. Application of the proposed prediction method to the case of Civil Aero-engine proved its effectiveness.

Abstract: This paper proposed a neural network (NN) based remaining useful life (RUL) prediction approach. A new performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, back propagation neural networks are trained for RUL prediction, and average of the networks’ outputs is considered as the final RUL in order to overcome prediction errors caused by random initiations of NNs. Finally, an experiment is set up based on a Bently-RK4 rotor unbalance test bed to validate the neural network based life prediction models, experimental results illustrate the effectiveness of the methodology.