The condition monitoring and fault diagnosis of rolling element bearings play an important role in the safe and reliable operation of rotating machinery. Feature extraction based on vibration signals is an effective means to identify the operating condition of rolling element bearings. Methods based on multi-scale mathematical morphology (MM) have recently been developed to extract features from one-dimensional signals. In this paper, a new double-dot structuring element (SE) is constructed for multi-scale MM. A pattern spectrum, obtained from the multi-scale MM, is used as a feature extraction index. A correlation analysis gives the final identification result by utilizing information over a whole pattern spectrum. Compared with the most commonly used flat SE, the double-dot SE can extract more features of original signals at different scales. Vibration signals, measured from defective bearings with outer race faults, inner race faults and ball faults, are used to evaluate the fault detection ability of the proposed SE and bearing fault diagnosis method. Results show that faults at different levels can be identified, including ball fault; and the location of outer race fault can also be differentiated.

No abstract

Isobaric vapor−liquid equilibrium (VLE) data for methanol−methyl ethyl ketone (MEK) systems containing ionic liquids (ILs) have been measured with a modified Othmer still at atmospheric pressure (101.32 kPa), and they were correlated by the NRTL equation. Three ILs, composed of an anion tetrafluoroborate ([BF4] − ) and a cation from 1-ethyl-3-methylimidazolium ([EMIM] + ), over 1-butyl-3-methylimidazolium ([BMIM] + ), to 1-octyl-3-methylimidazolium ([OMIM] + ), were investigated, and they all gave rise to a change of the relative volatility of methanol to MEK. The results indicated that, among the three ILs studied, [BMIM] + [BF 4 ] − and [OMIM] + [BF 4 ] − eliminated the azeotropic point at mole fraction 30 % and 10 %, respectively, whereas IL [EMIM] + -[BF 4 ] − pulled down the azeotropic point. The influence of the variation of the cation's alkyl chain length of imidazolium tetrafluoroborate-based ILs on VLE of the azeotropic system methanol−MEK was discussed.

No abstract

Mathematical morphology (MM) is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE). Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.