Innovative predictive maintenance concepts to improve life cycle management

For naval systems with typically long service lives, high sustainment costs and strict availability requirements, an effective and efficient life cycle management process is very important. In this paper four approaches are discussed to improve that process: physics of failure based predictive maintenance, advanced data analysis, condition based maintenance and maintenance optimization. For all these approaches, understanding of the failure behaviour and quantifying the effects of variations in usage of the system appear to be the key factor for improvements

The effect of maintenance policy violations

Motivation\ud Maintenance policy assessments usually rely on expert judgement. We seek for some history based validation. Organisations may use our inference to assess risks of maintenance policy violations. \ud \ud Approach\ud We depart from the arbitrary viewpoint that decisions have observable effects. We confine to a dependency between a maintenance policy and functionality. We implement (Granger, 1980)’s notion of prima facie causality that suits history based inferences under an observational study. A case study which is realistic in terms of sample size and operationalization allows us to reflect on inference precision.\ud \ud Achievements\ud We implemented an argument to infer a prima facie cause between a maintenance policy and functionality. We showed some delimitations of the applicability to any kind of maintenance policy violation and to the recordings from any organisation.\ud \ud Recommendations\ud - Reduce controversy about maintenance policy assessments.\ud - Improve integrity of maintenance recordings.\ud - Ensure that the sampling rate of performance indicators enables reconstruction of the signal

Experimental Evaluation of Vibration-Based Damage Identification Methods on a Composite Aircraft Structure with Internally-Mounted Piezodiaphragm Sensors

Maintenance strategies in various fields of industry, including aerospace applications, are shifting from time-scheduled to condition based strategies. An important requirement to allow this shift is to acquire knowledge on the failure modes and mechanisms of the system under observation. This implies for the aerospace industry that knowledge on composite failure modes, such as a typical skin-stiffener delamination, is essential. Prior research of the authors revealed the use of vibration based structural health monitoring, with application on laboratory specimen. The next step is to apply the methods developed to a more complex real aerospace structure. The objective of this study is to employ an internally-mounted piezo electric transducers based SHM strategy to a composite aerospace-related structure. Previous studies in laboratory-scale composite studies have revealed that delamination in a composite structure can be detected and localized by calculating the Modal Strain Energy (MSE) from vibration measurements of a pristine and damaged structure. In this study, a Carbon Fiber Reinforced Plastic (CFRP) aileron having a complex and representative aircraft geometry is used to evaluate the SHM approach where internally-mounted piezo diaphragms are used to calculate MSE damage indicator. The structure was excited by an electro-mechanical shaker inducing a 50 to 1000 Hz sine sweep. 19 piezo diaphragms, divided over two rows, are internally mounted on and next to a stringer where impact was applied to. The results show that the MSE damage indicator derived from the internal sensors can detect and (partly) localize the damage

Listening to Corrosion

Using condition monitoring techniques to achieve predictive maintenance is a prominent topic for military systems. Some of the main challenges related to this topic will be introduced, and after that a specific application will be used to demonstrate the successful development of a corrosion monitoring technique. One of the effective ways to cope with corrosion as a failure mechanism is to use dedicated sensors. Preferably, these sensors do not interfere with the prevalent corrosion process, i.e. they ‘listen to corrosion’ as it occurs spontaneously. A potentially interesting monitoring technique is based on electrochemical noise (EN), which is the spontaneous charge transfer generated by the corrosion process. A unique property of this technique is the possibility to identify corrosion processes based on their EN signature. This work describes the analysis of EN signals, based on which corrosion identification can be performed. Metastable pitting of AISI304 stainless steel serves as an example of the analysis procedure. The effectiveness of the procedure is then demonstrated by means of the identification of microbiologically influenced corrosion (MIC), which is generally regarded as one of the most difficult to predict corrosion mechanisms

Vibration based blind identification of bearing failures for autonomous wireless sensor nodes

Despite all the attention received by maintainers, undetected roller bearings failures are still a major source of concern in relation with reliability losses and high maintenance costs. Because of that, bearing condition assessment through vibration monitoring remains an intensive topic of scientific research, focusing on the definition of monitoring strategies that allow early stage damage detection, failure causes identification and remaining life prediction. Next to the developments on signal processing, new opportunities of advanced monitoring platforms are devised as those based on Wireless Sensor Networks (WSNs). The combination of integrated sensing, embedded computing and wireless communication provides interesting elements on the development of a new generation of vibration monitoring systems. The algorithms for bearing assessment remain a crucial point for achieving a balance between efficient monitoring strategies and highly flexible monitoring platforms. Though current trends on signal processing for mechanical vibrations focuses on the development of robust techniques, the constraints of embedded processing in relation to energy and memory consumption hamper their implementation on WSN.\ud The present paper discusses the problem of bearing condition characterization from the basis of extraction of damage features associated with the specific stage of its deterioration process. This, other than data driven methods, allow to find the best compromise between robustness of the bearing assessment algorithm and the applicability of the algorithm on a WSN. Two cases are presented as validation of this approach: an artificial damage on a lab setup and a train bearing, for which the possibilities for detection, diagnostics and prognostics are discussed. The advantages and constraints of the use of autonomous wireless sensor nodes is discussed as final part of the pape