Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing methods leverage advanced neural networks for prognostics performance improvement, providing mainly point ...estimates as prognostics results without addressing prognostics uncertainty. However, uncertainty is critical for both health prognostics and subsequent decision making, especially for safety-critical applications. Inspired by the idea of Bayesian machine learning, a Bayesian deep-learning-based (BDL-based) method is proposed in this paper for health prognostics with uncertainty quantification. State-of-the-art deep learning models are extended into Bayesian neural networks (BNNs), and a variational-inference-based method is presented for the BNNs learning and inference. The proposed method is validated through a ball bearing dataset and a turbofan engine dataset. Other than point estimates, health prognostics using the BDL-based method is enhanced with uncertainty quantification. Scalability and generalization ability of state-of-the-art deep learning models can be well inherited. Stochastic regularization techniques, widely available in mainstream software libraries, can be leveraged to efficiently implement the BDL-based method for practical applications.
Degradation modeling plays an important role in system health diagnosis and remaining useful life (RUL) prediction. Recently, a class of Wiener process models with adaptive drift was proposed for ...degradation-based RUL prediction, which has been proven flexible and effective. However, the existing studies use an autoregressive model of order 1 for the adaptive drift, which can result in difficulties in both model estimation and RUL prediction. This paper proposes a new adaptive Wiener process model that utilizes a Brownian motion for the adaptive drift. The new model shares the flexibility of the existing models, but avoids the difficulties in model estimation and RUL prediction. A model estimation procedure based on maximum likelihood estimation is developed, and the RUL prediction based on the proposed model is formulated. The effectiveness of the model in RUL prediction is validated using simulation and through an application to the lithium-ion battery degradation data.
Condition monitoring of the wind turbine based on supervisory control and data acquisition (SCADA) data has attracted much attention in recent years. Nevertheless, there are some inherent challenges ...in SCADA data analysis, including the low sampling rate, time-varying working conditions of the wind turbine, and a lack of historical fault data. To solve these problems, this article develops a novel condition monitoring and fault isolation system. First, a covariate-adjusted preprocessing procedure is proposed to account for the various working conditions of the wind turbine. Next, we construct a global monitoring statistic based on all temperature variables contained in the SCADA data, with a view to monitoring the overall health status of the wind turbine. If an alarm is raised, we isolate the fault through a variable selection method without relying on expert knowledge or historical fault data. Simulation and real cases are provided to demonstrate the effectiveness of this system.
This article systematically investigates the inverse Gaussian (IG) process as an effective degradation model. The IG process is shown to be a limiting compound Poisson process, which gives it a ...meaningful physical interpretation for modeling degradation of products deteriorating in random environments. Treated as the first passage process of a Wiener process, the IG process is flexible in incorporating random effects and explanatory variables that account for heterogeneities commonly observed in degradation problems. This flexibility makes the class of IG process models much more attractive compared with the Gamma process, which has been thoroughly investigated in the literature of degradation modeling. The article also discusses statistical inference for three random effects models and model selection. It concludes with a real world example to demonstrate the applicability of the IG process in degradation analysis. Supplementary materials for this article are available online.
For many products, it is not uncommon to see that a unit with a higher degradation rate has a more volatile degradation path. Motivated by this observation, we propose a new class of random effects ...model for the Wiener process model. We express the Wiener process in a special form and allow one of the parameters to be random across the product population so that a unit with a high degradation rate would also possess high volatility. Statistical inference of the model is discussed. By the same token, we introduce a stress–acceleration relation for the Wiener process so that both the degradation rate and the volatility of the product are increasing in the stress level. The proposed models are demonstrated by analyzing a dataset of fatigue crack growth and a dataset of head wears of hard disk drives. The applications suggest that our models perform better than existing models that ignore the positive correlation between the drift rate and the volatility.
•We propose a new Wiener process to capture dependence between mean and variance.•Random-effects Wiener process models are developed so that the variance is increasing in the degradation mean.•Stress are incorporated into the process in a similar way.•Statistical inference methods are also developed.•The methods are applied to two real examples.
In a physical system, components are usually installed in fixed positions that are known as operating slots. Due to such reasons as user behavior and imbalanced workload, a component's degradation ...can be affected by the corresponding installation position in the system. As a result, components degradation levels can be significantly different even when the components come from a homogeneous population. Dynamic reallocation of the components among the installation positions is a feasible way to balance the extent of the degradation, and hence, extend the time from system installation to its replacement. In this study, we quantify the benefit of incorporating reallocation into the condition-based maintenance framework for series systems. The degradation of components in the system is modeled as a multivariate Wiener process, where the correlation between the degradation is considered. Under the periodic inspection framework, the optimal control limits for reallocation and preventive replacement are investigated. We first propose a reallocation policy of two-component systems, where the degradation process with reallocation and replacement is formulated as a semi-regenerative process. Then the long-run average operational cost is computed based on the stationary distribution of its embedded Markov chain. We then generalize the model to general series systems and use Monte Carlo simulations to approximate the maintenance cost. The optimal thresholds for reallocation and replacement are obtained from a stochastic response surface method using a stochastic kriging model. We further generalize the model to the scenario of an unknown degradation rate associated with each slot. The proposed model is applied to the tire system of a car and the battery system of hybrid-electric vehicles, where we show that the reallocation policy is capable of significantly reducing the system's long-run average operational cost.
The mission abort is an effective action to reduce the risk of casualties and enhance the survivability of mission-based systems such as aircrafts, submarines, and unmanned aerial vehicles (UAVs). A ...main task in real operations is to strive for balance between the mission reliability and the system survivability via elaborate mission abort plans. In this paper, we design the optimal mission abort policies based on the information of early-warning signals, which indicates the possible forthcoming fatal malfunction. Depending on the acquisition time of such information, the operator may immediately abort the mission, or ignore the information and continue the task. Within the framework of a constant mission duration, we carry out an economic analysis for the above problem. The optimal abort decision that minimizes the expected total economic loss is investigated. We further extend the proposed model to the scenario of a random mission duration and derive the corresponding optimal abort decisions. A case study on a UAV executing power-grid inspection missions is used to illustrate the applicability of the abort policies.
Cordycepin(3'-deoxyadenosine), a cytotoxic nucleoside analogue found in
, has attracted much attention due to its therapeutic potential and biological value. Cordycepin interacts with multiple ...medicinal targets associated with cancer, tumor, inflammation, oxidant, polyadenylation of mRNA, etc. The investigation of the medicinal drug actions supports the discovery of novel targets and the development of new drugs to enhance the therapeutic potency and reduce toxicity. Cordycepin may be of great value owing to its medicinal potential as an external drug, such as in cosmeceutical, traumatic, antalgic and muscle strain applications. In addition, the biological application of cordycepin, for example, as a ligand, has been used to uncover molecular structures. Notably, studies that investigated the metabolic mechanisms of cordycepin-producing fungi have yielded significant information related to the biosynthesis of high levels of cordycepin. Here, we summarized the medicinal targets, biological applications, cytotoxicity, delivery carriers, stability, and pros/cons of cordycepin in clinical applications, as well as described the metabolic mechanisms of cordycepin in cordycepin-producing fungi. We posit that new approaches, including single-cell analysis, have the potential to enhance medicinal potency and unravel all facets of metabolic mechanisms of cordycepin in
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The accumulation of lipid peroxides is recognized as a determinant of the occurrence of ferroptosis. However, the sensors and amplifying process of lipid peroxidation linked to ferroptosis remain ...obscure. Here we identify PKCβII as a critical contributor of ferroptosis through independent genome-wide CRISPR-Cas9 and kinase inhibitor library screening. Our results show that PKCβII senses the initial lipid peroxides and amplifies lipid peroxidation linked to ferroptosis through phosphorylation and activation of ACSL4. Lipidomics analysis shows that activated ACSL4 catalyses polyunsaturated fatty acid-containing lipid biosynthesis and promotes the accumulation of lipid peroxidation products, leading to ferroptosis. Attenuation of the PKCβII-ACSL4 pathway effectively blocks ferroptosis in vitro and impairs ferroptosis-associated cancer immunotherapy in vivo. Our results identify PKCβII as a sensor of lipid peroxidation, and the lipid peroxidation-PKCβII-ACSL4 positive-feedback axis may provide potential targets for ferroptosis-associated disease treatment.
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