Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Breathe easier online : evaluation of a randomized controlled pilot trial of an internet-based intervention to improve well-being in children and adolescents with a chronic respiratory condition

Background Chronic respiratory illnesses are the most common group of childhood chronic health conditions and are overrepresented in socially isolated groups. Objective To conduct a randomized controlled pilot trial to evaluate the efficacy of Breathe Easier Online (BEO), an Internet-based problem-solving program with minimal facilitator involvement to improve psychosocial well-being in children and adolescents with a chronic respiratory condition. Methods We randomly assigned 42 socially isolated children and adolescents (18 males), aged between 10 and 17 years to either a BEO (final n = 19) or a wait-list control (final n = 20) condition. In total, 3 participants (2 from BEO and 1 from control) did not complete the intervention. Psychosocial well-being was operationalized through self-reported scores on depression symptoms and social problem solving. Secondary outcome measures included self-reported attitudes toward their illness and spirometry results. Paper-and-pencil questionnaires were completed at the hospital when participants attended a briefing session at baseline (time 1) and in their homes after the intervention for the BEO group or a matched 9-week time period for the wait-list group (time 2). Results The two groups were comparable at baseline across all demographic measures (all F < 1). For the primary outcome measures, there were no significant group differences on depression (P = .17) or social problem solving (P = .61). However, following the online intervention, those in the BEO group reported significantly lower depression (P = .04), less impulsive/careless problem solving (P = .01), and an improvement in positive attitude toward their illness (P = .04) compared with baseline. The wait-list group did not show these differences. Children in the BEO group and their parents rated the online modules very favorably. Conclusions Although there were no significant group differences on primary outcome measures, our pilot data provide tentative support for the feasibility (acceptability and user satisfaction) and initial efficacy of an Internet-based intervention for improving well-being in children and adolescents with a chronic respiratory condition. Trial registration Australian New Zealand Clinical Trials Registry number: ACTRN12610000214033;