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Air compressors have become critical equipment in different industrial applications such as metallurgy, mining, machinery manufacturing, petrochemical industry, transportation, etc. However, because of their complex structure and often harsh working environment, air compressors inevitably face a variety of faults and failures during their operation. Therefore, intelligent diagnostic techniques are crucially important for early fault recognition and detection to avoid industrial failure due to machine breakdowns. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is proposed based on several approaches, mainly: Maximal overlap discrete wavelet packet transform (MODWPT) and time domain features for feature extraction, weighted superposition attraction (WSA) for feature selection and random forest (RF), ensemble tree (ET) K-nearest neighbors (KNN) as classifiers. The proposed approach is applied to real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states. According to our approach, the data signals are decomposed by MODWPT into several nodes. Then, the time domain features are calculated for each node to construct the feature matrix for each air compressor health state. After that, WSA is applied to every matrix in the feature selection step. Finally, KNN, ET and RF are used to calculate the classification accuracy and give the confusion matrix. Compared with the robust empirical mode decomposition (REMD), the experimental results prove the effectiveness of the proposed approach to detect, identify and classify all air compressor faults.

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Due to their complexity and often harsh working environment, air compressors are inevitably exposed to a variety of faults and defects during their operation. Thus, condition monitoring is critically required for early fault recognition and detection to avoid any type industrial failures. In this paper, an intelligent algorithm for reciprocating air compressor fault diagnosis is developed using real-time acoustic signals acquired from an air compressor with one healthy and seven different faulty states such as leakage inlet valve (LIV), leakage outlet valve (LOV), non-return valve (NRV), piston ring, flywheel, rider-belt and bearing defects. The proposed algorithm mainly consists of three steps: feature extraction, selection, and classification. For feature extraction, experimental acoustic signals are decomposed using maximal overlap discrete wavelet packet transform (MODWPT) by six levels into 64 wavelet coefficients (nodes). Thereafter, time domain features are calculated for each node to build each air compressor’s health state feature matrix. Each feature matrix dimension is reduced by selecting the most useful features using Harris hawks optimization (HHO) in the feature selection step. Finally, for feature classification, selected features are used as inputs for random forest (RF), ensemble tree (ET) and K-nearest neighbors (KNN) to detect, identify, and classify the compressor health states with high classification accuracy. Comparative studies with several feature extraction and selection methods prove the proposed approach’s efficiency in detecting, identifying, and classifying all air compressor faults.

This paper addresses a multi-objective optimization problem for marine monitoring using USV. The objectives are to cover the maximum area with the lowest energy cost while avoiding collisions. The problem is solved using an exact and heuristic methods. First, a multi-objective Mixed Integer Programming formulation is proposed to model the USV monitoring problem. It consists of a combination of the Covering Salesman Problem (CSP) and Travelling Salesman Problem with Profit (TSPP). Then, we use CPLEX software to provide exact solutions. On the other hand, a customized chromosome-size algorithm is used to find heuristic solution. The latter is a multi-objective evolutionary algorithm known as Pareto Archived Evolution Strategy (PAES). The obtained results showed that the exact solving of the USV monitoring mission problem with mixed-integer programming (MIP) methods needs extensive computational costs. However, the customized PAES was able to provide Near-optimal solutions for large-size graphs in much faster time as compared to the exact one.