Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

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Background: Long-term care residents are susceptible to constipation and one-half to three quarter of older nursing home residents receive laxatives regularly. Objectives: The purpose of this study was to evaluate the factors related to abnormal bowel function and explore the effectiveness of laxative treatment among the elderly residents of a nursing home. Patients and Methods: A total of 110 residents older than 65 years old was enrolled in this study. The following variables were gathered: age, gender, body mass index (BMI), length of stay, daily fluid intake, type of food, functional level, cognitive ability, physical therapy status, somatic and psychiatric diseases, number of medications, and medication use. The use and dosage of laxatives were recorded by means of Anatomical Therapeutic Chemical (ATC) classification system. Normal bowel function was defined as defecation frequency from three defecations per day to three defecations per week and stool consistency score of three to five on Bristol Stool Form Scale. A comparison between groups with normal and abnormal bowel function was drawn. Results: Low BMI, increased fluid intake, liquid food intake, poor functional level, poor cognition, and a history of stroke were significantly associated with altered bowel function (P < 0.05). The most frequently used laxatives were glycerol, senna glycoside, and magnesium oxide. There were significant differences in laxative regimens between residents with normal and altered bowel function; those with altered bowel function tended to take more laxatives than those with normal bowel function. Conclusions: This study suggested that treatment of constipation in the nursing home was unsatisfactory. To improve treatment outcomes in those susceptible to altered bowel function, a coordinated approach with involvement of physicians, nursing staff, and other professionals including dieticians and pharmacists seems necessary. Keywords:Nursing Homes; Constipation; LaxativesImplication for health policy/practice/research/medical education: Treatment of constipation in the nursing home is unsatisfactory. For those who are susceptible to altered bowel function, a coordinated approach is necessary with the involvement of physicians, nursing staff, and other professionals including dieticians and pharmacists to improve the treatment outcomes.

Gearbox is the key functional unit in a mechanical transmission system. As its operating condition being complex and the interference transmitting from diverse paths, the vibration signals collected from an individual sensor may not provide a fully accurate description on the health condition of a gearbox. For this reason, a new method for fault diagnosis of gearboxes based on multi-sensor data fusion is presented in this paper. There are three main steps in this method. First, prior to feature extraction, two signal processing methods, i.e. the energy operator and time synchronous averaging, are applied to multi-sensor vibration signals to remove interference and highlight fault characteristic information, then the statistical features are extracted from both the raw and preprocessed signals to form an original feature set. Second, a coupled feature selection scheme combining the distance evaluation technique and max-relevance and min-redundancy is carried out to obtain an optimal feature set. Finally, the deep belief network, a novel intelligent diagnosis method with a deep architecture, is applied to identify different gearbox health conditions. As the multi-sensor data fusion technique is utilized to provide sufficient and complementary information for fault diagnosis, this method holds the potential to overcome the shortcomings from an individual sensor that may not accurately describe the health conditions of gearboxes. Ten different gearbox health conditions are simulated to validate the performance of the proposed method. The results confirm the superiority of the proposed method in gearbox fault diagnosis.