Within condition based maintenance (CBM), the whole aspect
of prognostics is composed of various tasks from multidimensional
data to remaining useful life (RUL) of the equipment.
Apart from data acquisition phase, data-driven prognostics
is achieved in three main steps: features extraction
and selection, features prediction, and health-state classification.
The main aim of this paper is to propose a way of improving
existing data-driven procedure by assessing the predictability
of features when selecting them. The underlying

Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting.

The use of condition monitoring (CM) in wind energy machines continues to evolve as wind energy machines grow in size and move offshore. Early and smaller wind generation machines offered little financial incentives for condition monitoring, justifying only simple and inexpensive health monitoring technologies. Today, multi-megawatt wind machines are more complex, more difficult to physically reach, and generate more revenue than previous models. This paper reviews challenges and candidate technologies for next generation condition monitoring in Wind Energy.

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic schemes (both model-based and data-driven) that attempt to forecast machinery health by constructing health propagation models for the underlying systems. In particular, algorithms that use the data-driven approach learn models directly from the data, rather than using a hand-built model based on human expertise.

Prognostics performance evaluation has gained significant attention in the past few years. As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics.

Aircraft engine bearing prognosis not only requires early detection of a bearing defect, but also the ability to predict bearing health conditions given certain operational scenarios. This paper summarizes a physics-based remaining useful life prediction method developed in the DARPA engine system prognosis (ESP) program. This investigation focuses on a typical roller bearing fault (or defect) on the outer raceway. Spall detection is based on the fusion of vibration and online oil debris sensors.

Fatigue crack initiation and growth during the service of aging aircraft are important life-limiting phenomena. In a previous study, a risk prediction and reliability model for naval aircraft has been developed based on fracture mechanics and inspection field data. Despite significant achievements in the study of fatigue cracks using fracture mechanics, it is still of great interest to find practical techniques for monitoring the crack growth using non-destructive inspection and to integrate the inspection results with the fracture mechanics models to improve the predictions.

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies.

Actuator systems are employed widely in aerospace, transportation and industrial processes to provide power to critical loads, such as aircraft control surfaces. They must operate reliably and accurately in order for the vehicle / process to complete successfully its designated mission. Incipient actuator failure conditions may severely endanger the operational integrity of the vehicle / process and compromise its mission.