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

Corrosion classification through deep learning of electrochemical noise time-frequency transient information

This paper for the first time treats the interpretation of electrochemical noise time-frequency spectra as an imageclassification problem. It investigates the application of a convolutional neural network (CNN) for deep learningimage classification of electrochemical noise time-frequency transient information. Representative slices of thesespectra were selected by our transient analysis technique and served as input images for the CNN. Corrosion datafrom two types of pitting corrosion processes serve as test cases: AISI304 and AA2024-T3 immersed in a 0.01MHCl and 0.1M NaCl solution between 0 and 1ks after immersion, respectively. Continuous wavelet transform(CWT) spectra and modulus maxima (MM) are used to train the CNN, either individually or in a combined form.The classification accuracy of the CNN trained with the combined dataset is 0.97 and with the two individualdatasets 0.72 (only CWT spectrum) and 0.84 (only MM). The ability to additionally classify a more progressedform of pitting corrosion of AA2024-T3 between 9 and 10ks after immersion indicates that the proposed methodis sufficiently robust using combined datasets with CWT spectra and MM. The pitting processes can effectively bedetected and classified by the proposed method. The most important contribution of the present work is tointroduce a novel procedure that decreases the classical need for large amounts of raw data for training andvalidation purposes, while still achieving a satisfactory classification robustness. A relatively small number ofindividual signals thereby generates a multitude of input images that still contain all relevant kinetic informationabout the underlying chemo-physical proces

Data-driven maintenance of military systems:Potential and challenges

The success of military missions is largely dependent on the reliability and availability of the systems that are used. In modern warfare, data is considered as an important weapon, both in offence and defence. However, collection and analysis of the proper data can also play a crucial role in reducing the number of system failures, and thus increase the system availability and military performance considerably. In this chapter, the concept of data-driven maintenance will be introduced. First, the various maturity levels, ranging from detection of failures and automated diagnostics to advanced condition monitoring and predictive maintenance are introduced. Then, the different types of data and associated decisions are discussed. And finally, six practical cases from the Dutch MoD will be used to demonstrate the benefits of this concept and discuss the challenges that are encountered in applying this in military practice

Electrochemical Noise: A Clear Corrosion Signature

The interpretation of electrochemical noise (EN) data has long been under discussion. Throughout the years, many data analysis techniques have been proposed for this purpose. As a starting point, procedures and parameters that enable identification of, or discrimination between, general and localized corrosion processes through EN are critically discussed. It is important to consider which type of information is required for a specific application. EN can be used to determine the barrier properties of a protective coating, and can therefore provide quantitative information about corrosion processes, although its most interesting ability is to provide information on corrosion characteristics. EN signals consist of a direct current (DC) component, or trend, with superimposed fluctuations. In electrochemical potential noise (EPN), this DC component represents the open corrosion potential (OCP) of the system under study. In electrochemical current noise (ECN), the DC component can e.g. be generated by electrode asymmetry. Any DC drift should be carefully removed prior to further data analysis. A procedure is introduced to accurately define the DC drift in EN signals using either of two time-frequency data analysis techniques: discrete wavelet transform (DWT) or empirical mode decomposition (EMD). Consistent and reliable information can be obtained from EN data when a data analysis procedure is selected that on the one hand has a high discrimination ability and on the other hand yields a descriptive parameter that is directly associated to the underlying physico- chemical process. Preferably, the information is obtained without the need for subjective a-priori limitations or assumptions concerning the nature of the process under investigation. These requirements are met by the Hilbert-Huang transform, which is based on the EMD. The result is a Hilbert spectrum in which local frequency information, so-called instantaneous frequencies, of EN signals, is visualized. The use of Hilbert spectra for the characterization of EN data in corrosion studies is introduced, based on the (general and localized) corrosion characteristics of carbon steel and stainless steel AISI304. A highly detailed decomposition of the original ECN and EPN data is provided in time and frequency simultaneously. This allows distinguishing between different corrosion characteristics based on their EN signals. Hilbert spectra also provide the possibility to analyze only transients present in the EN signals that originate from localized corrosion processes on AISI304. Initial identification of transients is based on the transient shape. Analysis of instantaneous frequency information present in these transients enables improved differentiation between corrosion characteristics as compared to data analysis using Hilbert spectra or energy distribution plots (determined from DWT) without transient analysis. The applicability of transient analysis through Hilbert spectra of ECN signals is further investigated for Ce-based inhibition of aluminium alloy AA2024-T3 and for detection and identification of microbiologically influenced corrosion (MIC). Transient analysis allows detection of changes in corrosion characteristics, i.e. the evolution of corrosion inhibition of AA2024-T3 by Ce-ions, with time. The initial procedure of transient detection is further developed, comprising of automatic detection of specific areas of interest in Hilbert spectra between 10E-1 Hz and 1 Hz, corresponding with the occurrence of transients in the respective ECN signals. Regarding the detection of MIC, together with monitoring of the OCP and microscopic observations, the development of ECN transients generated by localized corrosion processes could be attributed to the presence and activity of sulphate-reducing bacteria. These transients are related to the existence of pits in the carbon steel surface, underneath the attached biofilm. Finally, practical aspects and configurations for electrochemical noise measurements (ENM) are discussed. ENM can be applied in a hand-held solution, or for permanent monitoring. Analogous to the selection of the appropriate data analysis procedure to obtain the information of interest, it is important to consider the required application to select the most suitable configuration. ENM is a potentially interesting technique because of its non-intrusive nature, the robust sensor configurations, the ability to identify localized corrosion processes and ease of use. The most complicated aspect of ENM is the interpretation of the EN signals, for which the data analysis procedure can be fully automated if required.Materials Science & EngineeringMechanical, Maritime and Materials Engineerin

Requirements for corrosion inhibitor release from damaged primers for stable protection: A simulation and experimental approach using cerium loaded carriers

In this work a diffusion-driven inhibitor transport model is used to help in the design of inhibitor-loaded carriers for anticorrosive primers. The work focuses on inhibitor release at damaged locations of different dimensions exposed to electrolyte and is validated experimentally. The damage dimensions are first simulated to determine the minimal inhibitor release rate necessary to reach the required inhibitor concentrations for corrosion protection of the exposed metal. Kinematic and mass conservation laws are then used as first-order approximations to study the effect of different characteristics of nano- and micro-particles loaded with inhibitors embedded in an organic coating during the first 100 s of immersion. The simulated results are validated experimentally using epoxy coatings containing cerium-loaded zeolites and diatomaceous earth as nano- and micro-carriers respectively. These experiments confirmed the simulated predictions, showing that under the used exposure conditions nano-particles are only able to protect relatively small damages of micron size dimensions. Micron-sized carriers on the other hand allow sufficient release to protect larger damages, even at lower pigment volume concentrations. Additional simulations on rapid electrolyte diffusion pathways inside the coating are also in good agreement with the experiments, indicating the presence of diffusion pathways might play an important role in sustained inhibitor release and corrosion protection at local damages.Novel Aerospace MaterialsTeam Arjan Mo

Advanced predictive maintenance concepts based on the physics of failure

Military systems are operated in a variable way and operating conditions are generally quite demanding. At the same time, the requirements in terms of availability and reliability are high, and strict budget limitations make that the maintenance activities must be carefully planned and executed. The traditional way of planning maintenance on military systems is the experience-based approach. However, for systems that are operated in a variable way, this approach is associated with a lot of uncertainty. For that reason, more advanced predictive maintenance concepts are developed based on the underlying physical failure mechanisms. This approach, referred to as the model-based approach, will be presented in this paper. The generic framework will be discussed and the approach will be demonstrated using four separate case studies

Advanced predictive maintenance concepts based on the physics of failure

Military systems are operated in a variable way and operating conditions are generally quite demanding. At the same time, the requirements in terms of availability and reliability are high, and strict budget limitations make that the maintenance activities must be carefully planned and executed. The traditional way of planning maintenance on military systems is the experience-based approach. However, for systems that are operated in a variable way, this approach is associated with a lot of uncertainty. For that reason, more advanced predictive maintenance concepts are developed based on the underlying physical failure mechanisms. This approach, referred to as the model-based approach, will be presented in this paper. The generic framework will be discussed and the approach will be demonstrated using four separate case studies