Remaining useful life estimation based on nonlinear feature reduction and support vector regression

Research on the non-smooth problems in the nonlinear support vector regression. A nonlinear smooth support vector regression model is proposed. Using a generalized cubic spline function approach the non-smooth part in the support vector regression model. The model of the nonlinear smooth support vector regression is solved by BFGS-Armijo. Then, the approximation accuracy and the astringency of the generalized cubic spline function to the insensitive loss function were analyzed. As a result, we found the four-order and six times spline function's approximation effect is better than other smooth functions, and the nonlinear smooth support vector regression model, which be proposed in this paper is convergent.

Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression

Prognostics and health management (PHM) is one of the research hotspots in reliability, where remaining useful life (RUL) estimation is a typical application scenario. In this article, a feature learning method based on dynamic Bayesian networks (DBNs) is proposed to improve the RUL estimation accuracy of the regression models. The best feature set is obtained with the conditional dependencies represented by the DBN structure. A local modeling method is applied here to reduce the computation for high-order DBN construction. The strength of the connections between variables together with a contribution index of variables in the DBN structure are defined to represent the feature importance of the variables. Feature transfer is carried out with feature importance under different operating conditions for a further improvement. Nonlinear regression models such as support vector regression (SVR) and Gaussian mixture regression (GMR) are built based on the learned features and used to estimate the RUL. The turbofan engine dataset C-MAPSS is used to validate the effectiveness of the proposed method. Compared with other recent RUL estimation models, the proposed method has a faster modeling speed and higher prediction accuracy.

고장 예지 및 건전성 관리 기술(Prognostics and Health Management; PHM)은 시스템의 현재 상태를 진단하고 향후 발생 가능한 고장 시점을 신뢰성 있게 예지하는 기술로써 유지 보수 비용의 절감 및 시스템의 안정성 향상을 꾀하고자 하는 다양한 산업분야에서 활발하게 이용되고 있다. 스마트 그리드의 에너지 저장장치, 전기차, 스마트폰, 항공우주산업 등 광범위한 사용처에서 중요한 에너지원으로 사용되고 있는 배터리 또한 성능 저하 및 폭발의 위험성으로부터 자유로울 수 없기 때문에 이러한 고장 예지 및 건전성 관리 기술이 반드시 적용되어야 할 어플리케이션이다. 본 논문에서는 PHM의 기본적인 개념을 소개함과 동시에 배터리의 잔존 유효 수명(Remaining Useful Life; RUL)을 예측하는 각종 알고리즘 및 성능 평가 지표 서술에 초점을 맞추도록 한다. 더불어 배터리의 기능적 동작 원리 및 전기화학 기반의 모델링에 대한 설명을 통해 향후 잠재적인 가능성을 지닌 배터리의 전반적인 특성에 대한 깊은 이해 및 응용 기술에 대한 통찰력을 제시하고자 한다. Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

Prognostics and Health Management (PHM) is a multidisciplinary field aiming at maintaining physical systems in their optimal functioning conditions. The system under study is assumed to be monitored by sensors from which are obtained measurements reflecting the system’s health state. A health index (HI) is estimated to feed a data-driven PHM solution developed to predict the remaining useful life (RUL). In this paper, the values taken by an HI are assumed imprecise (IHI). An IHI is interpreted as a planar figure called polygon and a case-based reasoning (CBR) approach is adapted to estimate the RUL. This adaptation makes use of computational geometry tools in order to estimate the nearest cases to a given testing instance. The proposed algorithm called RULCLIPPER is assessed and compared on datasets generated by the NASA’s turbofan simulator (C-MAPSS) including the four turbofan testing datasets and the two testing datasets of the PHM’08 data challenge. These datasets represent 1360 testing instances and cover different realistic and difficult cases considering operating conditions and fault modes with unknown characteristics. The problem of feature selection, health index estimation, RUL fusion and ensembles are also tackled. The proposed algorithm is shown to be efficient with few parameter tuning on all datasets.

In order to evaluate the degradation state of the mechanical equipment and master the information of the remaining useful life (RUL) of the bearing accurately, this paper presents a method for predicting the remaining useful life of bearings based on mutual information (MI) and support vector regression (SVR) model. The proposed method includes two steps of online and offline, the offline step is used to build a degradation model of the bearing by learning, the online step uses the degradation model to predict the remaining useful life. By analyzing the experimental data of bearing full lifetime degradation, the results show that the method can effectively simulate the bearing degradation process and predict the remaining useful life of the bearing.

The heart of prognostics and health management (PHM) is to predict the equipment degradation evolution and, thus, its Remaining Useful Life (RUL). These predictions drive the decisions on the equipment Operation and Maintenance (O&M), and these in turn influence the equipment degradation evolution itself. In this paper, we propose a novel PHM framework based on Sequential Decision Problem (SDP), Artificial Neural Networks (ANNs) and Reinforcement Learning (RL), which allows properly considering this feedback loop for optimal sequential O&M decision making. The framework is applied to a scaled-down case study concerning a real mechanical equipment equipped with PHM capabilities. A comparison of the proposed framework with traditional PHM is performed.

Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.

Advanced control of large-scale wind turbines: Structural load reduction and lifetime control

In the era of smart manufacturing and advanced industrial systems, the high degree of integration and intelligence of equipment demands higher reliability and safety from systems. Existing methods often rely on historical data for Remaining Useful Life (RUL) prediction to achieve Prognostic and Health Management (PHM). However, the internal units of complex equipment exhibit significant spatial correlation and temporal diversity, making PHM for complex equipment a multidimensional challenge involving both temporal and spatial information, thereby severely limits the effectiveness of RUL prediction for complex systems. Addressing these challenges, this study introduces a multi-scale spatiotemporal attention network with adaptive relationship mining, specifically designed for the remaining useful life (RUL) prediction of such equipment. The core of the proposed method lies in the multi-scale feature perception module, which adeptly extracts varied scale features from multidimensional sensor data. Following this, an innovative adaptive relationship mining module is integrated to uncover multi-order coupling relationships between diverse sensors, enhancing the model’s predictive accuracy. Furthermore, a spatiotemporal attention module is employed to discern and emphasize crucial spatiotemporal correlations. To validate the effectiveness and superiority of the proposed method, the Commercial Modular Aero-propulsion System Simulation (C-MAPSS) dataset is employed for comprehensive performance evaluation, the IEEE 2012 PHM bearing dataset is also adopted to demonstrate the generalization and robustness of the proposed method. The results not only show a notable improvement over existing methods but also offer a more intuitive understanding through visual representations, marking a significant stride in enhancing the safety and efficiency of complex systems.

Predicting the Remaining Useful Life (RUL) of turbofan engines can prevent air disasters caused by component degradation. It is an important procedure in prognostics and health management (PHM). Therefore, a deep learning-based RUL prediction approach is proposed. The CMAPSS benchmark dataset is used to determine the RUL of aviation engines, focusing specifically on the FD001 and FD003 sub-datasets.In this study, we propose a CAELSTM (Convolutional Autoencoder and Attention-based LSTM) hybrid model for RUL prediction. First, the sub-datasets are preprocessed, and a piecewise linear degradation model is applied. The proposed model utilizes an autoencoder followed by an LSTM layer with an attention mechanism, which focuses on the most relevant components of the sequences. A fully connected layer of the convolutional neural network is used to further process the important features. Finally, the proposed model is evaluated and compared with other approaches. The results show that the model surpasses state-of-the-art methods, achieving RMSE values of 14.44 and 13.40 for FD001 and FD003, respectively. Other evaluation criteria, such as MAE and scoring, were also used, with MAE achieving values of 10.49 and 10.68 for FD001 and FD003, respectively. The scoring achieved values of 282.38 and 264.47 for the same sub-datasets. These results highlight the model's promise for improving prognostics and health management (PHM) systems, offering a dependable tool for predictive maintenance in aerospace and related fields. They also demonstrate the effectiveness and superiority of the model in enhancing aviation safety.

Aluminum electrolytic capacitor is one of the key components that restrict the life of DC-DC converter. Studying its deterioration analysis has advanced to realizing the prognostics and health management (PHM) technology of DC-DC converter. The main deterioration modes of aluminum electrolytic capacitor include the capacitance decrease and the equivalent series resistance (ESR) increases. These cause the ripple voltage to increase and eventually lead to the DC-DC converter failure. Some parameters of DC-DC converter (such as the ripple voltage, the ripple current, etc.) are selected as the feature signals of the aluminum electrolytic capacitor deterioration. By monitoring the changes of the feature signals, the aluminum electrolytic capacitor deterioration degree can be measured indirectly. Through theoretical analysis, this paper proposed a new idea to predict the remaining useful life (RUL) of DC-DC converter, that is, establishing the curve of the ripple voltage as the aluminum electrolytic capacitor deterioration and setting a warning point in the curve. When the change rate of the ripple voltage reached the warning point, the life of the DC-DC converter will come to an end. So, the RUL of the DC-DC converter can be predicted. In this paper, the buck converter is verified by simulation and physical experiments amid good agreements with the theory. Key words: Aluminum electrolytic capacitor, DC-DC converter, deterioration, capacitance, equivalent series resistance (ESR), ripple voltage, remaining useful life (RUL), prognostics and health management (PHM).

Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE

Aircraft are highly valuable assets and large budgets are spent in preventive and predictive maintenance programs. The application of PHM (Prognostics and Health Management) technologies can be a powerful decision support tool to help maintenance planners. The RUL (Remaining Useful Life) estimations obtained from a PHM system can be used in order to plan in advance for the repair of components before a failure occurs. However, when system architecture is not taken into account, the use of PHM information may lead the operator to rush to replace a component that would not affect immediately the operation of the system under consideration. This paper presents a methodology for decision support in maintenance planning with application in aeronautical systems. The methodology combines system architecture information and RUL estimations for all components comprised in the system under study, allowing the estimation of a RUL value for the whole system. This system level RUL (S-RUL) can be used as support information for identifying the best moment to repair a component. Also, when several components present high degradation levels, the proposed methodology can be used to define a set of components that, when repaired, will bring the whole system to a safe degradation level with lowest cost. A case study is used to illustrate the application of the methodology in a simplified aircraft electrical system.

This paper presents a hybrid prognostic approach which is applied on electro-thermally actuated MEMS valve. The method can be applied on different categories of MEMS to assess their health state, calculate the remaining useful life (RUL) and take appropriate decisions to anticipate the failures. In this paper a detailed description of the proposed method is presented. It consists of combining both degradation and nominal behavior models in order to estimate the current and future MEMS health states. A test bed for several MEMS is designed to acquire data in relation with the health state of the MEMS. The data processing obtained with this platform aims at deriving degradation models. Physical modeling is used to build the nominal behavior model of the valve. To show the effectiveness of the proposed method and in order to validate the implementation of Prognostics and Health Management (PHM) models on MEMS, two simulated degradations of the valve MEMS are considered.