Maximal information-based nonparametric exploration for condition monitoring data

The system condition of valuable assets such as power plants is often monitored with thousands of sensors. A full evaluation of all sensors is normally not done. Most of the important failures are captured by established algorithms that use a selection of parameters and compare this to defined limits or references. Due to the availability of massive amounts of data and many different feature extraction techniques, the application of feature learning within fault detection and subsequent prognostics have been increasing. They provide powerful results. However, in many cases, they are not able to isolate the signal or set of signals that caused a change in the system condition. Therefore, approaches are required to isolate the signals with a change in their behavior after a fault is detected and to provide this information to diagnostics and maintenance engineers to further evaluate the system state. In this paper, we propose the application of Maximal Information-based Nonparametric Exploration (MINE) statistics for fault isolation and detection in condition monitoring data. The MINE statistics provide normalized scores for the strength of the relationship, the departure from monotonicity, the closeness to being a function and the complexity. These characteristics make the MINE statistics a good tool for monitoring the pair-wise relationships in the condition monitoring signals and detect changes in the relationship over time. The application of MINE statistics in the context of condition monitoring is demonstrated on an artificial case study. The focus of the case study is particularly on two of the MINE indicators: the Maximal information coefficient (MIC) and the Maximum Asymmetry Score (MAS). MINE statistics prove to be particularly useful when the change of system condition is reflected in the relationship between two signals, which is usually difficult to be captured by other metrics

Deep feature learning network for fault detection and isolation

Prognostics and Health Management (PHM) approaches typically involve several signal processing and feature engineering steps. The state of the art on feature engineering, comprising feature extraction and feature dimensionality reduction, often only provides specific solutions for specific problems, but rarely supports transferability or generalization: it often requires expert knowledge and extensive intervention. In this paper, we propose a new integrated feature learning approach for jointly achieving fault detection and fault isolation in high-dimensional condition monitoring data. The proposed approach, based on Hierarchical Extreme Learning Machines (HELM) demonstrates a good ability to detect and isolate faults in large datasets comprising signals of different natures, non-informative signals, non-linear relationships and noise. The method includes stacked auto-encoders that are able to learn the underlying high-level features, and a one-class classifier to combine the learned features in an indicator that represents the deviation from the normal system behavior. Once a deviation is identified, features are used to isolate the most deviating signal components. Two case studies highlight the benefits of the approach: First, a synthetic dataset with the typical characteristics of condition monitoring data and different types of faults is applied to evaluate the performance with objective metrics. Second, the approach is tested on data stemming from a power plant generator interturn failure. In both cases, the results are compared to other commonly applied approaches for fault isolation

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”Det brinner i knutarna”- En kvalitativ studie om socialarbetares upplevelser av arbetet med gravida missbrukare.

The purpose of this study was to find out how social workers handle and experience working with substance abusing women who are pregnant. We were interested in finding out how they experience their role as social workers in their professional relationship with the woman and how they look at the pregnancy in relation to motivation. Further on, we wanted to know their thoughts of how collaboration between professionals works and their opinions on LVM for pregnant women who are substance abusers. To achieve our purpose we interviewed nine social workers, of whom six are persons in authority and three have working assignments that are more counseling. Thereafter we used power as an analyzing perspective, focusing on how the relationship between the social worker and the woman is uneven in terms of power. Our main results were that the social workers sometimes find it tough to work with women who have a substance abuse during pregnancy and that it is important to be aware of and clear with the fact that the social worker has a lot of power. They often experience that the pregnancy itself is a source of motivation for these women to stop using alcohol and/or drugs. Some of the social workers thought that there should be a change in the law to protect the fetus, while some thought it to be unnecessary and mean that the existing law gives enough protection. Lastly, the social workers experience collaboration as well functioning although they believe that there are a relatively high number of women who never come to the social services knowledge

Physics-informed machine learning for predictive maintenance : applied use-cases

The combination of physics and engineering information with data-driven methods like machine learning (ML) and deep learning is gaining attention in various research fields. One of the promising practical applications of such hybrid methods is for supporting maintenance decision making in the form of condition-based and predictive maintenance. In this paper we focus on the potential of physics-informed data augmentation for ML algorithms. We demonstrate possible implementations of the concept using three use cases, differing in their technical systems, their algorithms and their tasks ranging from anomaly detection, through fault diagnostics up to prognostics of the remaining useful life. We elaborate on the benefits and prerequisites of each technique and provide guidelines for future practical implementations in other systems

On the sustainable use and conservation of plant genetic resources in Europe. Report from Work Package 5 “Engaging the user Community” of the

PGR Secure project , a collaborative project funded under the EU Seventh Framework Programme, THEME KBBE.2010.1.1-03, 'Characterization of biodiversity resources for wild crop relatives to improve crops by breeding', Grant agreement no. 266394.