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The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.

The authors would like to add the following note to Figure 3 of their paper published in Energies [...]

This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS.

For maintenance of gas turbines (GTs) in oil and gas applications, capital parts are removed and replaced by parts of the same type taken from the warehouse. When the removed parts are found not broken, they are repaired at the workshop and returned to the warehouse, ready to be used in future maintenance. The management of this flow is of great importance for the profitability of a GT plant. In this paper, we adopt a previously developed formalized framework of the part flow and reinforcement learning (RL) to optimize part flow management. The formal framework and RL algorithm are extended to account for the stochastic failure process of the involved parts. An application to a scaled-down case study derived from an industrial application is illustrated.

Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (1) Detect abnormal conditions and classify the type of anomaly; (2) recognize novel plant behaviors; (3) select representative examples of the novel classes for labeling by an expert; (4) automatically update the CM model. A CM model based on the never-ending learning paradigm is developed. It develops a dictionary containing labeled prototypical subsequences of signal values representing normal conditions and anomalies, which is continuously updated by using a dendrogram to identify groups of similar subsequences of novel classes and to select those subsequences to be labelled by an expert. A 1-nearest neighbor classifier is trained to online detect abnormal conditions and classify their types. The proposed CM model is applied to a synthetic case study and a real case study concerning the monitoring of the tank pressure of an aero derivative gas turbine lube oil system. The CM model provides satisfactory performances in terms of classification accuracy, while remarkably reducing the expert efforts for data labeling and model (periodic) updating.

In mental health promotion, recovery is a process that leads to personal strengthening, control over crucial life decisions, and participation in communities through relevant professional, educational, or family social roles. Co-production, a key aspect of the recovery-oriented approach, emphasizes collaboration and active participation of people with mental health first-hand experience, family members, and citizens. Even though studies on co-production are limited and fragmented, there is evidence that co-production leads to positive outcomes, including improved well-being, empowerment, social connectedness, inclusion, and personal competencies. This study aimed to contribute to the limited literature on co-production in mental health by evaluating the co-production process in a non-profit mental health organization and its impact on empowerment processes and personal recovery outcomes. The research team adopted a collaborative approach and conducted qualitative research, including 13 individual semi-structured interviews and four focus groups. Results showed how the different dimensions of empowerment are promoted in and by the organization: (a) co-production processes supported empowered outcomes on an individual level, such as self-awareness; (b) the organization was perceived to promote empowering processes, such as a sense of safeness and protection; (c) co-production was a mean to build and maintain a network with mental health services that acknowledges the dignity and value of each subjectivity and promotes participation and recovery. Peer support workers were seen as facilitators of mental illness management, and the organization as a place for sharing mental health experiences and fostering individual recovery journeys.

AbstractThe Editors-in-Chief are retracting this article [1].

There is compelling evidence that psychological factors may have the same or even greater impact on the possibility of adverse events on cardiac diseases (CD) than other traditional clinical risk factors. Anxiety and depression are predictors of short- and long-term adverse outcomes, increased risk for higher rates of in-hospital complications, re-infarction, malignant arrhythmias, and mortality in CD patients. Despite researchers finding that cognitive behavior therapy (CBT) reduced depressive and anxiety symptoms, the fact that such results are maintained only in the short term and the lack of maintenance of the long-term affects the absence of changes in lifestyles, preventing the possibility of a wide generalization of results. Recently wellbeing therapy (WBT) has been proposed as a useful approach to improve healthy lifestyle behaviors and reduce psychological distress. The present randomized controlled study will test WBT, in comparison with CBT, as far as the reduction of symptoms of depression, anxiety and psychological distress, and the improvement of lifestyle behaviors and quality of life in cardiac patients are concerned. Moreover, innovations in communication technologies allow patients to be constantly followed in real life. Therefore WBT based on personalized mobile technology will allow the testing of its effectiveness in comparison with usual WBT. The present study is a large outpatient study on the treatment of co-morbid depression, anxiety, and psychological distress in cardiac patients. The most important issues of this study are its randomized design, the focus on promotion of health-related behaviors, and the use of innovative technologies supporting patients' wellbeing in real life and in a continuous way. First results are expected in 2012. ClinicalTrials.gov Identifier: NCT01543815.

Background Inadequate blood pressure control and poor adherence to treatment remain among the major limitations in the management of hypertensive patients, particularly of those at high risk of cardiovascular events. Preliminary evidence suggests that home blood pressure telemonitoring (HBPT) might help increasing the chance of achieving blood pressure targets and improve patient’s therapeutic adherence. However, all these potential advantages of HBPT have not yet been fully investigated. Methods/design The purpose of this open label, parallel group, randomized, controlled study is to assess whether, in patients with high cardiovascular risk (treated or untreated essential arterial hypertension - both in the office and in ambulatory conditions over 24 h - and metabolic syndrome), long-term (48 weeks) blood pressure control is more effective when based on HBPT and on the feedback to patients by their doctor between visits, or when based exclusively on blood pressure determination during quarterly office visits (conventional management (CM)). A total of 252 patients will be enrolled and randomized to usual care ( n =84) or HBPT ( n =168). The primary study endpoint will be the rate of subjects achieving normal daytime ambulatory blood pressure targets (<135/85 mmHg) 24 weeks and 48 weeks after randomization. In addition, the study will assess the psychological determinants of adherence and persistence to drug therapy, through specific psychological tests administered during the course of the study. Other secondary study endpoints will be related to the impact of HBPT on additional clinical and economic outcomes (number of additional medical visits, direct costs of patient management, number of antihypertensive drugs prescribed, level of cardiovascular risk, degree of target organ damage and rate of cardiovascular events, regression of the metabolic syndrome). Discussion The TELEBPMET Study will show whether HBPT is effective in improving blood pressure control and related medical and economic outcomes in hypertensive patients with metabolic syndrome. It will also provide a comprehensive understanding of the psychological determinants of medication adherence and blood pressure control of these patients. Trial registration Clinical Trials.gov: NCT01541566

Background There is compelling evidence that psychological factors may have the same or even greater impact on the possibility of adverse events on cardiac diseases (CD) than other traditional clinical risk factors. Anxiety and depression are predictors of short- and long-term adverse outcomes, increased risk for higher rates of in-hospital complications, re-infarction, malignant arrhythmias, and mortality in CD patients. Despite researchers finding that cognitive behavior therapy (CBT) reduced depressive and anxiety symptoms, the fact that such results are maintained only in the short term and the lack of maintenance of the long-term affects the absence of changes in lifestyles, preventing the possibility of a wide generalization of results. Recently wellbeing therapy (WBT) has been proposed as a useful approach to improve healthy lifestyle behaviors and reduce psychological distress. Methods/design The present randomized controlled study will test WBT, in comparison with CBT, as far as the reduction of symptoms of depression, anxiety and psychological distress, and the improvement of lifestyle behaviors and quality of life in cardiac patients are concerned. Moreover, innovations in communication technologies allow patients to be constantly followed in real life. Therefore WBT based on personalized mobile technology will allow the testing of its effectiveness in comparison with usual WBT. Discussion The present study is a large outpatient study on the treatment of co-morbid depression, anxiety, and psychological distress in cardiac patients. The most important issues of this study are its randomized design, the focus on promotion of health-related behaviors, and the use of innovative technologies supporting patients’ wellbeing in real life and in a continuous way. First results are expected in 2012. Trial registration ClinicalTrials.gov Identifier: NCT01543815.