A new concept referred to as progression-based prediction of remaining life (PPRL) is proposed in the present paper in order to solve the problem of accurately predicting the remaining bearing life. The basic concept behind PPRL is to apply different prediction methods to different bearing running stages. A new prediction procedure which predicts precisely the remaining bearing life is developed on the basis of variables characterizing the state of a deterioration mechanism which are determined from on-line measurements and the application of PPRL via a compound model of neural computation. The procedure consists of on-line modelling of the bearing running state via neural networks and logic rules and not only can solve the boundary problem of remaining life but also can automatically adapt to changes in environmental factors. In addition, multi-step prediction is possible. The proposed technique enhances the traditional prediction methods of remaining bearing life.

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During the condition monitoring of a planetary gearbox, features are extracted from raw data for a fault diagnosis. However, different features have different sensitivity for identifying different fault types, and thus, the selection of a sensitive feature subset from an entire feature set and retaining as much of the class discriminatory information as possible has a directly effect on the accuracy of the classification results. In this paper, an improved hybrid feature selection technique (IHFST) that combines a distance evaluation technique (DET), Pearson's correlation analysis, and an ad hoc technique is proposed. In IHFST, a temporary feature subset without irrelevant features is first selected according to the distance evaluation criterion of DET, and the Pearson's correlation analysis and ad hoc technique are then employed to find and remove redundant features in the temporary feature subset, respectively, and hence, a sensitive feature subset without irrelevant or redundant features is selected from the entire feature set. Further, the k-means clustering method is applied to classify the different kinds of health conditions. The effectiveness of the proposed method was validated through several experiments carried out on a planetary gearbox with incipient cracks seeded in the tooth root of the sun gear, planet gear, and ring gear. The results show that the proposed method can successfully distinguish the different health conditions of a planetary gearbox, and achieves a better classification performance than other methods. This study proposes a sensitive feature subset selection method that achieves an obvious improvement in terms of the accuracy of the fault classification.

A sensitive and simple flow-injection chemiluminescence (FI-CL) method, which was based on the CL intensity generated from the redoxreaction of potassium permanganate (KMnO4)-formaldehyde in vitriol (H2SO4) medium, has been developed, validated and applied for the determination of naphazoline hydrochloride and oxymetazoline hydrochloride. Besides oxidants and sensitizers, the effect of the concentration of H(2)SO(4), KMnO4 and formaldehyde was investigated. Under the optimum conditions, the linear range was 1.0 x 10(-2)-7.0 mg/L for naphazoline hydrochloride and 5.0 x 10(-2)-10.0 mg/L for oxymetazoline hydrochloride. During seven repeated inter-day and intra-day precision tests of 0.1, 1.0 and 10.0 mg/L samples, the relative standard deviations all corresponded to reference values. The detection limit was 8.69 x 10(-3) mg/L for naphazoline hydrochloride and 3.47 x 10(-2) mg/L for oxymetazoline hydrochloride (signal-to-noise ratio < or = 3). This method has been successfully implemented for the determination of naphazoline hydrochloride and oxymetazoline hydrochloride in pharmaceuticals.