Organisational learning as structuration: an analysis of worker-led organisational enquiries in an oil refinery

First paragraph: Based on a three-year empirical investigation of an oil refinery, this chapter analyses organisational learning in terms of structuration theory. Structuration is the dynamic process by which an organisation’s rules and resources constrain individuals, while simultaneously enabling them to create new rules and resources. This was accomplished in the refinery by small groups of workers who conducted organisational enquiries into how to achieve the organisation’s purposes. The results were adopted as new organisational structures (norms, policies and procedures), which were then enacted by the workers. Through structuration, workers were able to exercise agency in redesigning their own work processes, and they emerged as self-directed, autonomous learners, who were knowledgeable about the grounds of their activity

Organisational learning as structuration: an analysis of worker-led organisational enquiries in an oil refinery

First paragraph: Based on a three-year empirical investigation of an oil refinery, this chapter analyses organisational learning in terms of structuration theory. Structuration is the dynamic process by which an organisation’s rules and resources constrain individuals, while simultaneously enabling them to create new rules and resources. This was accomplished in the refinery by small groups of workers who conducted organisational enquiries into how to achieve the organisation’s purposes. The results were adopted as new organisational structures (norms, policies and procedures), which were then enacted by the workers. Through structuration, workers were able to exercise agency in redesigning their own work processes, and they emerged as self-directed, autonomous learners, who were knowledgeable about the grounds of their activity

Interprofessionalism and the Collective Dimensions of Professional Practice

In recent years, most professions have come under increasing pressure to work more collaboratively than before. In particular, they have been encouraged to participate in various kinds of interprofessional collaboration which entail relinquishing some of their professional autonomy. This paper discusses the collective competence that underpins interprofessionalism in these situations, and the kinds of professional education and training that help to develop it

Classification of partial discharge EMI conditions using permutation entropy-based features

In this paper we investigate the application of feature extraction and machine learning techniques to fault identification in power systems. Specifically we implement the novel application of Permutation Entropy-based measures known as Weighted Permutation and Dispersion Entropy to field Electro- Magnetic Interference (EMI) signals for classification of discharge sources, also called conditions, such as partial discharge, arcing and corona which arise from various assets of different power sites. This work introduces two main contributions: the application of entropy measures in condition monitoring and the classification of real field EMI captured signals. The two simple and low dimension features are fed to a Multi-Class Support Vector Machine for the classification of different discharge sources contained in the EMI signals. Classification was performed to distinguish between the conditions observed within each site and between all sites. Results demonstrate that the proposed approach separated and identified the discharge sources successfully

Total Hip Joint Replacement Biotelemetry System

The development of a biotelemetry system that is hermetically sealed within a total hip replacement implant is reported. The telemetry system transmits six channels of stress data to reconstruct the major forces acting on the neck of the prosthesis and uses an induction power coupling technique to eliminate the need for internal batteries. The activities associated with the telemetry microminiaturization, data recovery console, hardware fabrications, power induction systems, electrical and mechanical testing and hermetic sealing test results are discussed

Naive bayes multi-label classification approach for high-voltage condition monitoring

This paper addresses for the first time the multilabel classification of High-Voltage (HV) discharges captured using the Electromagnetic Interference (EMI) method for HV machines. The approach involves feature extraction from EMI time signals, emitted during the discharge events, by means of 1D-Local Binary Pattern (LBP) and 1D-Histogram of Oriented Gradients (HOG) techniques. Their combination provides a feature vector that is implemented in a naive Bayes classifier designed to identify the labels of two or more discharge sources contained within a single signal. The performance of this novel approach is measured using various metrics including average precision, accuracy, specificity, hamming loss etc. Results demonstrate a successful performance that is in line with similar application to other fields such as biology and image processing. This first attempt of multi-label classification of EMI discharge sources opens a new research topic in HV condition monitoring

Entropy-based feature extraction for electromagnetic discharges classification in high-voltage power generation

This work exploits four entropy measures known as Sample, Permutation, Weighted Permutation, and Dispersion Entropy to extract relevant information from Electromagnetic Interference (EMI) discharge signals that are useful in fault diagnosis of High-Voltage (HV) equipment. Multi-class classification algorithms are used to classify or distinguish between various discharge sources such as Partial Discharges (PD), Exciter, Arcing, micro Sparking and Random Noise. The signals were measured and recorded on different sites followed by EMI expert’s data analysis in order to identify and label the discharge source type contained within the signal. The classification was performed both within each site and across all sites. The system performs well for both cases with extremely high classification accuracy within site. This work demonstrates the ability to extract relevant entropy-based features from EMI discharge sources from time-resolved signals requiring minimal computation making the system ideal for a potential application to online condition monitoring based on EMI