Prognostics and Health Management technologies are useful for early fault detection and optimization of reliability in mechanical systems. Reciprocating compressors units are commonly used in industry for gas pressurization and transportation, and the valves in compressors are considered vulnerable parts susceptible to failure. Then, early detection of faults is important for avoiding catastrophic accidents. A feasible approach for fault detection consists in measuring the vibration signal for extracting useful features enabling fault detection and classification. In this research, a test-bed composed by two-stage reciprocating compressor was used for simulating a set of 13 different conditions of combined faults in valves and roller bearings. Three accelerometers were used for collecting the vibration signals for extracting three different types of features. These features were analyzed furthermore by using two random forest models to classifying the different faults. The first set of features was obtained by applying the symbolic dynamics algorithm, which provides the histogram of a set of symbols. This set of symbols was obtained by subdividing a 2D Poincaré plot into angular regions and counting the intersection of the phase trajectories on each of regions. The second type of features corresponds to the complex correlation measure which is calculated as the addition of the areas of triangles belonging to a Poincaré plot. Additionally, a small set of classical statistical features was also used for comparing their classification abilities to the new set of proposed features. The three sets of features enable highly accurate classification of the set of faults when used with random forest classification models. Notably, the ensemble subspace k-Nearest Neighbors algorithm provides classification accuracies higher than 99%.

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

Although mechatronic systems are nowadays implemented in a large number of systems in vehicles, active lubrication systems are still incipient in industrial applications. This study is an attempt to extend the active lubrication concept to combustion engines and gives a theoretical contribution to this field. One refers to active lubrication when conventional hydrodynamic lubrication is combined with dynamically modified hydrostatic lubrication. In this study, two different schemes for the oil injection system in actively lubricated main engine bearings are presented. The use of active lubrication in journal bearings helps to enhance the hydrodynamic fluid film by increasing the fluid film thickness and consequently reducing viscous friction losses and vibrations. In this study, the hydrostatic lubrication is modified by injecting oil at controllable pressures through orifices circumferentially located around the bearing surface. The main equations that govern the dynamics of the injection for a piezo-actuated oil injector and a mechanical-actuated oil injector are presented. It is shown how the dynamics of the oil injection system is coupled to the dynamics of the bearing fluid film through equations. The global system is numerically solved using as a case study a single-cylinder combustion engine, where the conventional lubrication of the main bearing is modified by applying radial oil injection using piezo-actuated injection. The performance of such a hybrid bearing is compared to an equivalent conventional lubricated bearing in terms of the maximum fluid film pressures, minimum fluid film thicknesses, and reduction of viscous friction losses.

Compressors and pumps are machines frequently used in petroleum and chemical industries for fluid transportation through flow systems to keep industrial processes running permanently. As their failure can produce costly disruption, developing fault detection and diagnosis tools is essential for accurately detecting and diagnosing faults. This research proposes a bi-dimensional representation of the vibration signal corresponding to the Mel Frequency Cepstral Coefficients (MFCC) and their first two derivatives as features. The pseudo-periodic nature of the fault signature in rotating machines is exploited to put forward an efficient and accurate patch-wise fault classification method. This approach enables the classification of 13 combined types of faults in a multi-stage centrifugal pump and 17 faults in a reciprocating compressor. Classification is performed using the Long Short-Term Memory (LSTM) network, the bidirectional Long Short-Term Memory (BiLSTM) neural network, and the Convolutional Neural Network (CNN). Accurate classification over 99% is attained, showing that the proposed feature extraction procedure correctly classifies a large set of faults simultaneously appearing in such rotating machines.

The pyrometallurgical copper extraction process generates 2.2 t of residues for each ton of produced metal. Initially, this paper shows the physical and chemical characterization of a copper processing residue, followed by the procedure to obtain the gold and silver that are contained in the copper residue. The characterization of a white metal oxidized calcine indicates a high content of copper as cupric ferrite, and important values of gold and silver. Gold and silver concentrations were 38.5 g t-1 and 1085 g t-1, respectively. The gold was found scattered in cupric ferrite grains, while the silver was dissolved in a matrix of Cu-S in amounts between 1 % and 50 %. Cyanidation tests showed that 95 % gold recovery could be obtained with a concentration of 0.06 g l-1 KCN, a pH of 10.5, room temperature, and a residence time of 80 h. When the temperature was increased to 75 °C, and the other cyanidation parameters were maintained intact, the same recovery was obtained but with a shorter residence time. Silver recovery was less than 30 % with a concentration of 1.2 g l-1 KCN, pH of 10.5, temperature of 45 °C, and a residence time longer than 72 h.