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

1,3-Di-1-adamantylimidazolium (phthalocyaninato)lithium(I) acetone hemisolvate monohydrate

The asymmetric unit of the title compound, (C23H33N2)[Li(C32H16N8)]·0.5C3H6O·H2O, consists of two symmetry-unrelated lithium phthalocyanine (LiPc−) half-anions, centered at (1,0,0) and (0,,0), respectively, the bis­(adamant­yl)imidazolium cation (BAI+), occupying a general site, an acetone mol­ecule, disordered about the inversion centre at (0, , ) and a water mol­ecule at a general site. The LiPc− anions pack in a stepped pattern enclosing the bis­(adamant­yl)imidazolium cation. Attractions between the anion and cation are mediated by a water mol­ecule which forms O—H⋯N hydrogen bonds. In addition, two C—H⋯O interactions are seen

Utilizing Force-State Mapping for Detecting Fatigue Damage Precursors in Aerospace Applications

The Force-State-Mapping (FSM) method is utilized to identify and monitor precursors to fatigue damage in aerospace alloy structures exposed to vibratory loads. The method is created originally to detect nonlinearity in a dynamical system via a direct non-parametric identification. In this paper, FSM is constructed at various stages of the fatigue-life using the Masri-Caughey method. The experimental results show that FSM is a sensitive indicator for monitoring the health state of a structure prior to the development of cracks. The required restoring force due to fatiguing is obtained as a function of the vibration cycles and input loads. FSM appears to be a promising method in connecting the global structural dynamic response to the evolution in the micro-behavior of the materials due to fatigue degradation. For aerospace applications, the objective of this effort is to estimate the required restoring force using current structural health monitoring systems and supply this value to the control laws of an aircraft. Thus, an aircraft can prevent or slow crack development by autonomously readjusting its maneuver based on its health-state

Measuring electrochemical noise of a single working electrode for assessing corrosion resistance of polymer coated metals

Electrochemical noise measurement (ENM) of the spontaneous perturbation of current and potential of coated samples immersed in electrolyte determines the resistance of the coating system. ENM offers several advantages: the measurement is relatively simple to make, it is completely non-interfering with the natural process occurring on the surface and the data are simple to interpret. The original standard arrangement for ENM using a pair of samples has limitations for practical applications because two separate and nominally identical working electrodes are needed and this requirement is very hard (if not impossible) to fulfil in on-site application. This paper describes an alternative approach for electrochemical noise measurement to measure the noise resistance (R n) of protective coatings based on use of just one working electrode. In this so-called "Single Cell" (SC) arrangement the electrochemical noise current and electrochemical noise potential between the working electrode and a non-noisy reference electrode is measured separately and consecutively. This new approach has been tested for a range of coating resistances. Also, the coating's resistance has been measured using DC resistance and EIS (at low frequency) and the results were compared with the Rn obtained from the single cell (SC) set up. © 2014 Elsevier B.V

A critical review of electrochemical noise measurement as a tool for evaluation of organic coatings

The simplicity of measuring equipment and versatility of data analysis makes electrochemical noise measurement an ideal technique for acquiring electrochemical information about the corrosion behavior of a painted metal relatively quickly. Hence the method has great potential for use in the laboratory as well as in field situations. However, special care must be taken in choosing data acquisition parameters, reference electrodes and symmetry of electrodes in order to achieve reproducible measurements. These areas have been discussed in this review along with methods of data analysis, alternative electrode configurations for on-site measurements and novel applications of the technique