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A build-in data inversion method to retrieve aerosol size distributions based on the principle of particle electrical mobility has been introduced, whose required computational effort is low for a portable ultrafine particle sizer (PUPS). The PUPS is cost-effective for the measurements of the fine and ultrafine particles in polluted environments near-source (e.g., cities with high traffic density, freeways, airports or stationary combustion sources). The particle sizer is mainly composed of a unipolar charger, a plate differential mobility analyzer (PDMA), and a Faraday cup electrometer (FCE). The classification efficiencies of the PDMA are strongly dependent on the charging distribution of the unipolar charger, in which multiple charging is more significant than that for a bipolar charger. To reduce the excessive overlap in the kernel function caused by multiple charging, a guidance method for selecting the classification voltages and operating parameters of the PDMA is proposed with the help of MATLAB. Subsequently, a combination of the nonnegative least squares (NNLS) algorithm and post facto smoothing method has been employed to derive the discretized solution of the Fredholm integral equation of the first kind. In the end, the accuracy and stability of the proposed approach are tested under a range of particle size distribution scenarios. The synthesized data results show that for the unimodal aerosol distribution, almost all of the relative errors are less than 20 %, regardless of nonideal operating conditions. The inversion algorithm can be run on Cortex-M3, an ARM embedded chip for low-cost and low-power consumption applications. When a reasonable error range can be permitted, the inversion algorithm can meet the requirements of the PUPS.

The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representation-based Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, time-domain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequency-domain, wavelet-domain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representation-based Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequency-domain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and wavelet-domain classification of six fault-types gearbox vibration data from our rotating machinery experimental platform.

The effect of the design and process parameters of a particle-classification zone on the sizing precision of a plate differential mobility analyzer (PDMA) was investigated by simulation software in this work. The design parameters consisted of the length, width, and height of the classification zone, and the process parameters could lead to height deviation and an uneven classification-zone wall. Prior to investigating the parameters, a comparison between experimental voltages and PDMA simulation results was applied to validate the simulation model. Thanks to velocity-field and particle-trajectory simulations, as well as experimental data from the former version of the PDMA, the dimensions of the classification zone and other key parameters were optimized. Our research shows that the dimensions of the classification zone influence the applied voltages and the measuring range of the PDMA, and range can be extended to 500 nm with low applied voltages after optimization. When the reasonable error range of the height is &plusmn, 2% for the classification zone, the PDMA could basically achieve a classification role, and when it is &plusmn, 1%, the PDMA has a better classifying effect.