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In rotary machines, the bearing failure is one of the major causes of the breakdown of machinery. The bearing degradation monitoring is a great anxiety for the prevention of bearing failures. This paper aims to present a combination of the stationary wavelet decomposition and extra-trees regression (ETR) for the evaluation of bearing degradation.

The higher order cumulants features are extracted from the bearing vibration signals by using the stationary wavelet decomposition (stationary wavelet transform [SWT]). The extracted features are then subjected to the ETR for obtaining normal and failure state. A dominance level curve build using the dissimilarity data of test object and retained as health degradation indicator for the evaluation of bearing health.

Experiment conducts to verify and assess the effectiveness of ETR for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, it is compared with the performance of random forest regression and multi-layer perceptron regression.

The experimental results indicated that the presently adopted method shows better performance for detecting the degradation more accurately at early stage. Furthermore, the diagnostics and prognostics have been getting much attention in the field of vibration, and it plays a significant role to avoid accidents.

The purpose of this paper is to describe a novel methodology for predicting reliability for consumer electronics or any other hardware systems that experience a complex lifecycle environmental profile.

This Physics-of-Failure–based three-step methodology can be used to predict the degradation rate of a population using a Monte Carlo approach. The three steps include: using an empirical equation describing the degradation of a performance metric, a degradation consistency condition and a technique to account for cumulative degradation across multiple life-cycle stress conditions (e.g. temperature, voltage, mechanical load, etc.).

Two case studies are provided to illustrate the methodology including one related to repeated touch-load induced artifacts for displays.

This novel methodology can be applied to a wide range of applications from mechanical systems to electrical circuits. The results can be fed into the several stages of engineering validation to speed up product qualification.

This paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency.

A literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed.

Each maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented.

The literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard.

The novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed.

The traditional maintenance scheduling strategies of multi-component systems may result in maintenance shortage or overage, while system degradation information is often ignored. The purpose of this paper is to propose a multi-phase model that better integrates degradation information, dependencies and maintenance at the tactical level.

This paper proposes first a maintenance optimization model for multi-component systems with economic dependence and structural dependence. The cost of combining maintenance activities is lower than that of performing maintenance on components separately, and the downtime cost can be reduced by considering structural dependence. Degradation information and multiple maintenance actions within scheduling horizon are considered. Moreover, the maintenance resources can be integrated into the optimization model. Then, the optimization model adopting one maintenance activity is extended to multi-phase optimization model of the whole system lifetime by taking into account the cost and the expected number of downtime.

The superiority of the proposed method compared with periodic maintenance is demonstrated. Thus, the values of both integrated degradation information and considering dependencies are testified. The advantage of the proposed method is highlighted in the cases of high system utilization, long maintenance durations and low maintenance costs.

Few studies have been carried out to integrate decisions on degradation, dependencies and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic multi-phase model is proposed in this paper, along with an iterative solution algorithm for it.

This paper aims to investigate the reusability of metal/metal oxide-coupled ZnO nanorods (ZnO NRs) to degrade rhodamine B (RhB).

ZnO NRs particles were synthesized by precipitation method and used to remove various types of metal ions such as Cu²⁺, Ag⁺, Mn²⁺, Ni²⁺, Pb²⁺, Cd²⁺ and Cr²⁺ ions under UV illumination. The metal/metal oxide-coupled ZnO NRs were characterized by scanning electron microscope, X-ray diffraction and UV-Vis diffuse reflectance. The photodegradation of RhB dye by these metal/metal oxide-coupled ZnO NRs under UV exposure was assessed.

The metal/metal oxide-coupled ZnO NRs were successfully reused to remove RhB dye in which more than >90% of RhB dye was degraded under UV exposure. Furthermore, the coupling of Ag, CuO, MnO₂, Cd and Ni particles onto the surface of ZnO NRs even enhanced the degradation of dye. The dominant reactive species involved in the degradation of RhB dye were ^•OH- and ^•O₂⁻-free radicals.

The coupling of metal/metal oxide onto the surface of ZnO NRs after metal ions removal could affect the photocatalytic performance of ZnO NRs in the degradation of organic pollutants in subsequent stage.

A good reusability performance of metal/metal oxide-coupled ZnO NRs make ZnO NRs become a desirable photocatalyst material for the treatment of wastewater, which consists of both heavy metal ions and organic dyes.

Metal/metal oxide coupling onto the surface of ZnO NRs particles improved subsequent UV-assisted photocatalytic degradation of RhB dye.

The purpose of this paper is to provide an investigation on a new kind of photocatalytic material, namely, the porous ceramic foam loading titanium dioxide, which can make an effective photocatalytic degradation of the methyl orange (MO) solution in the wastewater.

The natural zeolite powder has been used as the primary raw material to produce a sort of lightweight porous ceramic foam by impregnating polymer foam in slurry and then sintering. With the sol-gel method, a kind of open-cell reticular porous ceramic foam loading TiO₂ film was obtained having a good photocatalytic action, and the resultant porous composite product presents the bulk density of 0.3~0.6 g/cm³ to be able to float on water.

The MO could tend to be completely degraded in the solution with a certain concentration by the TiO₂-loaded ceramic foam irradiated with ultraviolet light, and this composite foam was found to have high degradation efficiency for the MO solution in a wide range of pH.

This work presents a TiO₂-loaded ceramic foam that can effectively photo-catalyze to degrade the MO in water, and the degradation efficiency were examined under different conditions of the MO solution with various pH values.

A reliable and safe operation of fuel cells (FCs) is imperative for their application in aviation, especially within the main powertrain. Moreover, performance and lifetime requirements for technical and economic viability are demanding compared to their stationary or road transportation counterparts, while the operating conditions are considered challenging. Prognostics and health management (PHM) could represent a powerful tool for enhancing reliability, durability and performance by detecting, predicting and/or mitigating relevant degradation and failure mechanisms. Against this backdrop, the authors consider it of high relevance to obtain an understanding of the effectiveness of PHM approaches for polymer electrolyte fuel cells (PEFCs) for future aircraft applications, which represents the aim of this paper.

In this study, the authors first discuss application relevant failure modes, review state-of-the-art PHM approaches and, consecutively, assess the potential of FC control strategies for aviation. Aiming for a tangible, comparable metric for this initial assessment, the authors apply a published remaining useful life prediction method to load profiles for a range of aviation-specific applications.

The authors’ analysis shows significant potentials for lifetime improvement by (partial) avoidance of high power operation and rapid load change through control strategies. Tapping into these theoretical potentials, however, requires significant developments in the field of PEFC PHM and a focus on aviation specific degradation and performance testing.

The novelty of this study lies in creating an understanding of the potential of avoiding or preventing certain degradation modes by means of PHM in the PEFC specifically in aviation applications.

A reliability analysis of a solid oxide fuel cell (SOFC) system is presented for applications with strict constant power supply requirements, such as data centers. The purpose is to demonstrate the effect when moving from a module-level to a system-level in terms of reliability, also considering effects during start-up and degradation.

In-house experimental data on a system-level are used to capture the behavior during start-up and normal operation, including drifts of the operation point due to degradation. The system is assumed to allow replacement of stacks during operation, but a minimum number of stacks in operation is needed to avoid complete shutdown. Experimental data are used in conjunction with a physics-based performance model to construct the failure probability function. A dynamic program then solves the optimization problem in terms of time and replacement requirements to minimize the total negative deviation from a given target reliability.

Results show that multi-stack SOFC systems face challenges which are only revealed on a system- and not on a module-level. The main finding is that the reliability of multi-stack SOFC systems is not sufficient to serve as sole power source for critical applications such as data center.

The principal methodology may be applicable to other modular systems which include multiple critical components (of the same kind). These systems comprise other electrochemical systems such as further fuel cell types.

The novelty of this work is the combination of mathematical modeling to solve a real-world problem, rather than assuming idealized input which lead to more benign system conditions. Furthermore, the necessity to use a mathematical model, which captures sufficient physics of the SOFC system as well as stochasticity elements of its environment, is of critical importance. Some simplifications are, however, necessary because the use of a detailed model directly in the dynamic program would have led to a combinatorial explosion of the numerical solution space.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

With the rapid development and stable operated application of lithium-ion batteries used in uninterruptible power supply (UPS), the prediction of remaining useful life (RUL) for lithium-ion battery played an important role. More and more researchers paid more attentions on the reliability and safety for lithium-ion batteries based on prediction of RUL. The purpose of this paper is to predict the life of lithium-ion battery based on auto regression and particle filter method.

In this paper, a simple and effective RUL prediction method based on the combination method of auto-regression (AR) time-series model and particle filter (PF) was proposed for lithium-ion battery. The proposed method deformed the double-exponential empirical degradation model and reduced the number of parameters for such model to improve the efficiency of training. By using the PF algorithm to track the process of lithium-ion battery capacity decline and modified observations of the state space equations, the proposed PF + AR model fully considered the declined process of batteries to meet more accurate prediction of RUL.

Experiments on CALCE dataset have fully compared the conventional PF algorithm and the AR + PF algorithm both on original exponential empirical degradation model and the deformed double-exponential one. Experimental results have shown that the proposed PF + AR method improved the prediction accuracy, decreases the error rate and reduces the uncertainty ranges of RUL, which was more suitable for the deformed double-exponential empirical degradation model.

In the running of UPS device based on lithium-ion battery, the proposed AR + PF combination algorithm will quickly, accurately and robustly predict the RUL of lithium-ion batteries, which had a strong application value in the stable operation of laboratory and other application scenarios.