Non-target environmental analysis by liquid chromatography/high-resolution mass spectrometry with a product ion and neutral loss database

Despite the increasing detection of emerging substances in the environment, the identity of most are left unknown due to the lack of efficient identification methods. We developed a non-target analysis method for identifying unknown substances in the environment by liquid chromatography/high-resolution mass spectrometry (LC/HRMS) with a product ion and neutral loss database (PNDB). The present analysis describes an elucidation method with elemental compositions of the molecules, product ions, and corresponding neutral losses of the unknown substance: (1) with the molecular formula, possible molecular structures are retrieved from two chemical structure databases (PubChem and ChemSpider); then (2) with the elemental compositions of product ions and neutral losses, possible partial structures are retrieved from the PNDB; and finally, (3) molecular structures that match the possible partial structures are listed in order of number of hits. A molecular structure with a higher number of hits is more similar to the structure of the analyzed substance. The performance of the non-target method was evaluated by simulated analysis of 150 LC/HRMS spectra registered in MassBank. First, all substances of the same mass data (41/41) and 68% (39/57) of the mass data of the same substances not registered in the PNDB were elucidated. It was demonstrated that 14% (7/52) and 31% (16/52) of the substances with no mass spectral data registered in the PNDB were obtained at the first and within the fifth place, respectively. Owing to the fact that 10 of the total hits occurred in product ions and neutral losses, almost 50% of the substances evaluated with this method were placed at the top 4 positions in the similarity ranking. Importantly, the proposed method is effective for analyzing mass spectral data that has not been registered in the PNDB and thus is expected to be used for a variety of non-target analyses.

Fault Identification for a Closed-Loop Control System Based on an Improved Deep Neural Network

Fault identification for closed-loop control systems is a future trend in the field of fault diagnosis. Due to the inherent feedback adjustment mechanism, a closed-loop control system is generally very robust to external disturbances and internal noises. Closed-loop control systems often encourage faults to propagate inside the systems, which may lead to the consequence that faults amplitude becomes smaller and fault characteristics difference becomes more inapparent. Hence, it has been challenging to achieve fault identification for such systems. Traditional fault identification methods are not particularly designed for closed-loop control systems and thus cannot be applied directly. In this work, a new fault identification method is proposed, which is based on the deep neural network for closed-loop control systems. Firstly, the fault propagation mechanism in closed-loop control systems is theoretically derived, and the influence of fault propagation on system variables is analyzed. Then deep neural network is applied to find fault characteristics difference between different data modes, and a sliding window is used to amplify the fault-to-noise ratio and characteristics difference, with an aim to increase the identification performance. To verify this method, the simulations that are based on a numerical simulation model, the Tennessee industrial system and the satellite attitude control system are conducted. The results show that the proposed method is more feasible and more effective in fault identification for closed-loop control systems compared with traditional data-driven identification methods, including distance-based and angle-based identification methods.