A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)

Enabling participation of Black and Minority Ethnic (BME) and seldom-heard communities in health research: A case study from the SCAMP adolescent cohort study

Our inquiry investigated the barriers to, and facilitators for, the involvement of Black and Minority Ethnic (BME) and ‘seldom-heard’ communities, in a study researching the impact of mobile phone and wireless device usage on adolescents’ cognition, behaviour and mental health. The aim was to co-produce solutions to increase participation, and we used focus groups, telephone interviews, a community event and a public and patient involvement (PPI) café to conduct the inquiry. Five themes emerged from the data: two enablers – the value and benefits of research; and three barriers – concerns about research and about communication, and practical constraints. A central cross-cutting theme, the concept of trust, was evident from the data, and extended across all themes, including across the solutions to nonparticipation. When the data collection and analysis were completed, we ran a symposium for researchers and members of the public to share our findings and to co-produce solutions. The symposium generated ideas about improving participation, including tailoring participant information, engaging with local advocates and involving people in research design and delivery

Mixed methods process evaluation of my breathing matters, a digital intervention to support self-management of asthma.

This study aimed to explore user engagement with 'My Breathing Matters', a digital self-management intervention for asthma, and identify factors that may influence engagement. In a mixed methods design, adults with asthma allocated to the intervention arm of a feasibility trial (n = 44) participated in semi-structured interviews (n = 18) and a satisfaction questionnaire (n = 36) to explore their views and experiences of the intervention. Usage data highlighted that key intervention content was delivered to most users. The majority of questionnaire respondents (78%; n = 28) reported they would recommend the intervention to friends and family. Interviewees expressed positive views of the intervention and experienced several benefits, mainly improved asthma control, medication use, and breathing technique. Factors that may influence user engagement were identified, including perceptions of asthma control, current self-management practices, and appeal of the target behaviours and behaviour change techniques. Findings suggested My Breathing Matters was acceptable and engaging to participants, and it was used as intended

The population biology of the living coelacanth studied over 21 years

Between 1986 and 2009 nine submersible and remote-operated vehicle expeditions were carried out to study the population biology of the coelacanth Latimeria chalumnae in the Comoro Islands, located in the western Indian Ocean. Latimeria live in large overlapping home ranges that can be occupied for as long as 21 years. Most individuals are confined to relatively small home ranges, resting in the same caves during the day. One hundred and forty five coelacanths are individually known, and we estimate the total population size of Grande Comore as approximately 300–400 adult individuals. The local population inhabiting a census area along an 8-km section of coastline remained stable for at least 18 years. Using LASER-assisted observations, we recorded length frequencies between 100 and 200 cm total length and did not encounter smaller-bodied individuals (\100 cm total length). It appears that coelacanth recruitment in the observation areas occur mainly by immigrating adults. We estimate that the mean numbers of deaths and newcomers are 3–4 individuals per year, suggesting that longevity may exceed 100 years. The domestic fishery represents a threat to the long-term survival of coelacanths in the study area. Recent changes in the local fishery include a decrease in the abundance of the un-motorized canoes associated with exploitation of coelacanths and an increase in motorized canoes. Exploitation rates have fallen in recent years, and by 2000, had fallen to lowest ever reported. Finally, future fishery developments are discussed

“I’d rather wait and see what’s around the corner”: a multi-perspective qualitative study of treatment escalation planning in frailty

Introduction People living with frailty risk adverse outcomes following even minor illnesses. Admission to hospital or the intensive care unit is associated with potentially burdensome interventions and poor outcomes. Decision-making during an emergency is fraught with complexity and potential for conflict between patients, carers and clinicians. Advance care planning is a process of shared decision-making which aims to ensure patients are treated in line with their wishes. However, planning for future care is challenging and those living with frailty are rarely given the opportunity to discuss their preferences. The aim of the ProsPECT (Prospective Planning for Escalation of Care and Treatment) study was to explore perspectives on planning for treatment escalation in the context of frailty. We spoke to people living with frailty, their carers and clinicians across primary and secondary care. Methods In-depth online or telephone interviews and online focus groups. The topic guide explored frailty, acute decision-making and planning for the future. Data were thematically analysed using the Framework Method. Preliminary findings were presented to a sample of study participants for feedback in two online workshops. Results We spoke to 44 participants (9 patients, 11 carers and 24 clinicians). Four main themes were identified: frailty is absent from treatment escalation discussions, planning for an uncertain future, escalation in an acute crisis is ‘the path of least resistance’, and approaches to facilitating treatment escalation planning in frailty. Conclusion Barriers to treatment escalation planning include a lack of shared understanding of frailty and uncertainty about the future. Emergency decision-making is focussed on survival or risk aversion and patient preferences are rarely considered. To improve planning discussions, we recommend frailty training for non-specialist clinicians, multi-disciplinary support, collaborative working between patients, carers and clinicians as well as broader public engagement

Development of an Automated Fault Detection and Diagnosis tool for AHU's

Heating Ventilation and Air Conditioning (HVAC) system energy consumption on average accounts for 40percent of an industrial sites total energy consumption. Studies have indicated that 20 - 30 percent energy savings are achievable by re-commissioning HVAC systems to rectify faulty operation with savings of over 20 percent of total energy cost possible by continuously commissioning. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults and their causes in physical systems. AFDD can be used to identify faults in HVAC systems with a view to reducing their energy consumption. An AFDD tool has been designed and developed to allow the performance analysis of AHU's by utilizing knowledge-based principles. Based on an initial alpha testing phase on 12 AHU's across four large industrial pilot sites, in excess of 120,000 euro of energy savings have been detected by the AFDD tool and verified by site survey

Development of an Online Expert Rule Based Automated fault Detection and Diagnostic (AFDD) Tool for Air Handling Units: Beta Test Results

Heating Ventilation and Air Conditioning (HVAC) system energy consumption accounts for an average of 40% of an industrial sites energy consumption. Studies have indicated that 20 - 30% energy savings are achievable by recommissioning Air Handling Units (AHU) in HVAC systems to rectify faulty operation. Studies have also demonstrated that continuous commissioning of building systems for optimum efficiency can yield savings of an average of over 20% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults and their causes in physical systems. AFDD can help support multiple stages in the commissioning process. This paper outlines the development of an AFDD tool for AHU's using expert rules then details the results of its beta testing phase on twenty-six AHU's across six large commercial & manufacturing sites. To date, validated energy savings of over 157,000 have been identified by the AFDD tool

Patients' perceptions of the potential of breathing training for asthma: a qualitative study.

Poor symptom control is common in asthma. Breathing training exercises may be an effective adjunct to medication; it is therefore important to understand facilitators and barriers to uptake of breathing training exercises