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

The relation between thinking and mood in daily life:The effects of content and context of thought

The association between thought content and mood in daily life is far from established. The aim of the present investigation was to examine the role of content and context of thought in daily life mood (i) concurrent and across time, and (ii) as simple effects and as interactions between them. Participants were 50 university students (82% female), who completed experience sampling assessments for a week. Linear mixed-effects models showed that time and object aspects of thought were significantly associated with concurrent mood. In addition, interaction effects between object of thought and thought context (activity) significantly predicted concurrent, but not future, mood, sometimes showing a switch from a positive to a negative association in certain contexts. It is concluded that associations between thought content and mood in daily life (i) are depending on the activity context, and (ii) seem to be relatively short-lived in most cases

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

The relation between thinking and mood in daily life: The effects of content and context of thought

The association between thought content and mood in daily life is far from established. The aim of the present investigation was to examine the role of content and context of thought in daily life mood (i) concurrent and across time, and (ii) as simple effects and as interactions between them. Participants were 50 university students (82% female), who completed experience sampling assessments for a week. Linear mixed-effects models showed that time and object aspects of thought were significantly associated with concurrent mood. In addition, interaction effects between object of thought and thought context (activity) significantly predicted concurrent, but not future, mood, sometimes showing a switch from a positive to a negative association in certain contexts. It is concluded that associations between thought content and mood in daily life (i) are depending on the activity context, and (ii) seem to be relatively short-lived in most cases

Safety behaviors toward innocuous stimuli can maintain or increase threat beliefs

Safety behaviors can prevent or minimize a feared outcome. However, in relatively safe situations, they may be less adaptive, presumably because people will misattribute safety to these behaviors. This research aimed to investigate whether safety behaviors in safe situations can lead to increased threat beliefs. In Study 1, we aimed to replicate a fear conditioning study (N = 68 students) in which the experimental, but not the control group, received the opportunity to perform safety behavior to an innocuous stimulus. From before to after the availability of the safety behavior, threat beliefs persisted in the experimental group, while they decreased in the control group. In Study 2, we examined whether threat beliefs had actually increased for some individuals in the experimental group, using a multi-dataset latent class analysis on data from Study 1 and two earlier studies (N = 213). Results showed that about a quarter of individuals who performed safety behavior toward the innocuous stimulus showed increased threat expectancy to this cue, while virtually nobody in the control group exhibited an increase. Taken together, safety behavior in relatively safe situations may have maladaptive effects as it generally maintains and sometimes even increases threat beliefs

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