Classification of bearing faults through time-frequency analysis and image processing

The present work proposes a new technique for bearing fault classification that combines time-frequency analysis with image processing. This technique uses vibration signals from bearing housings to detect bearing conditions and classify the faults. By means of Empirical Mode Decomposition (EMD), each vibration signal is decomposed into Intrinsic Mode Functions (IMFs). Principal Components Analysis (PCA) is then performed on the matrix of the decomposed IMFs and the important principal components are chosen. The spectrogram is obtained for each component by means of the Short Time Fourier Transform (STFT) to obtain an image that represents the time-frequency relationship of the main components of the analyzed signal. Furthermore, Image Moments are extracted from the spectrogram images of principal components in order to obtain an array of features for each signal that can be handled by the classification algorithm. 8 images are selected for each signal and 17 moments for each image, so an array of 136 features is associated with every signal. Finally, the classification is performed using a standard machine learning technique, i.e. Support Vector Machine (SVM), in the proposed technique. The dataset used in this work include data collected for various rotating speeds and loads, in order to obtain a set of different operating conditions, by a Roller Bearing Faults Simulator. The results have shown that the developed technique provides classification effectively, with a single classifier, of bearing faults characterized by different rotating speeds and different loads

Exploiting heterogeneous data for the estimation of particles size distribution in industrial plants

In industrial environments, it is often difficult and expensive to collect a good amount of data to adequately train expert systems for regression purposes. Therefore the usage of already available data, related to environments showing similar characteristics, could represent an effective approach to find a good balance between regression performance and the amount of data to gather for training. In this paper, the authors propose two alternative strategies for improving the regression performance by using heterogeneous data, i.e. data coming from diverse environments with respect to the one taken as reference for testing. These strategies are based on a standard machine learning algorithm, i.e. the Artificial Neural Network (ANN). The employed data came from measurements in industrial plants for energy production through the combustion of coal powder. The powder is transported in air within ducts and its size is detected by means of Acoustic Emissions (AE) produced by the impact of powder on the inner surface of the duct. The estimation of powder size distribution from AE signals is the task addressed in this work. Computer simulations show how the proposed strategies achieve a relevant improvement of regression performance with respect to the standard approach, using ANN directly on the dataset related to the reference plant

Power plant condition monitoring by means of coal powder granulometry classification

In this work, a condition monitoring approach suitable for coal fired power plant is proposed. This approach is based on classification techniques and it is applied for the monitoring of the Particle Size Distribution (PSD) of coal powder. For coal fired power plant, the PSD of coal can affect the combustion performance, therefore it is a meaningful parameter of the operating condition of the plant. Three tests have been carried out aimed to study the effect of the class numbers, the dataset size, and the reduction of the number of false positives on the effectiveness of the approach. For each designed test, three standard classification algorithms, i.e. Artificial Neural Network, Extreme Learning Machine and Support Vector Machine, have been employed and compared. Experimental data taken from 13 measuring point on 13 burners of two different industrial power plants have been used. Obtained results showed that, using two classes give the most accurate results, using only the 90% of the available data can still provide comparable classification results, and the level of false positive can be effectively reduced

Future Aircraft and the Future of Aircraft Noise

In order to cope with increasing air traffic and the requirement to decrease the overall footprint of the aviation sector - making it more sustainably and acceptable for the whole society - drastic technology improvements are required beside all other measures. This includes also the development of novel aircraft configurations and associated technologies which are anticipated to bring significant improvements for fuel burn, gaseous and noise emissions compared to the current state and the current evolutionary development. Several research projects all over the world have been investigating specific technologies to address these goals individually, or novel - sometimes also called "disruptive" - aircraft concepts as a whole. The chapter provides a small glimpse on these activities - mainly from a point of view of recent European funded research activities like Horizon2020 projects ARTEM, PARSIFAL, and SENECA being by no-way complete or exhaustive. The focus of this collection is on noise implications of exemplary novel concepts as this is one of the most complicated and least addressed topics in the assessment of aircraft configurations in an early design stage. Beside the boundary layer ingestion concept, the design process for a blended wing body aircraft is described, a box-wing concept is presented and an outlook on emerging supersonic air transport is given

Direct investigation of liquid bridges in relation to the mechanisms of particle agglomeration in gaseous and liquid media

Particle agglomeration processes occur in a wide variety of important industrial applications, either intentionally, for product formulation purposes (e.g. pharmaceutical granulation) or unintentionally, as in the caking of powder in storage silos. Examples of agglomerated products are: fertilisers, ceramics, catalysts, pesticides, minerals, pharmaceuticals, foods (particularly 'instant' products) and detergents, where the common objective is to improve the handling and ease of use of the product. Agglomeration can also be used as a separation technique. This is often carried out in solid/liquid suspensions, where either the desired or undesired particle species are encouraged to agglomerate by the addition of surfactants and electrolytes, so that they can then be separated from the remaining gangue. The process is referred to as spherical agglomeration, because of the final spherical geometry of the formed agglomerates; in this case the liquid binder is chosen to be immiscible with the suspending medium. Spherical agglomeration is used in the separation of minerals and valuable ores as small as 10 m in diameter with a high grade of recovery, as well as in the manufacture of speciality chemical products. In both agglomeration and spherical agglomeration processes the presence of particles of different wetting behaviour can create selective agglomeration of some particles at the expense of others and this phenomenon may be either beneficial, as in a selective recovery process, or undesired in the agglomeration of a formulated product. Moreover, in liquid media both the particles and the liquid binder assume a charge that can be altered by the addition of surfactants. Such conditions can largely modify the particle-to-binder interaction before any liquid bridge is formed. This thesis reports on the extensive experimental programme undertaken to study the physiochemical properties of liquid bridges formed between pairs of particles of similar/dissimilar surface energy that are either surrounded by air or are submerged in a liquid medium. The objective was to elucidate the fundamental mechanisms governing the initial stages of agglomeration in relation to the wetting behaviour exhibited by the particles and, for the case of a suspending liquid medium, the conditions (addition of surfactant/electrolyte) that may improve the affinity of the particle toward the liquid binder. The experimental work on liquid bridges was carried out with a unique Micro-Force Balance (MFB), which was developed in previous work to measure liquid bridge forces in gaseous media. The present work describes the continued development of the measurement technique and its adaptation to the measurement and observation of interparticle forces in liquid media. The principal parameters investigated were the geometry (in both gaseous and liquid bulk media), the strength and the energy (in the liquid bulk medium) of a liquid bridge during separation of particles exhibiting similar or dissimilar surface energies. In the case of the experiments carried out in a liquid medium, the influence that surfactants have on the particle wetting behaviour and on the adhesiveness of the liquid bridge was also investigated. The force exerted by a liquid bridge was largely influenced by the wetting behaviour of the liquid on the particle. Liquids that welted both particles well produced the highest forces. This phenomenon may explain the reason why during agglomeration of different species particles with higher wettability can stay together whilst those with lower affinity toward the binder may separate again and be segregated under the influence of agitation. The affinity of particles toward the liquid binder and its relation with the presence of surfactant/electrolytes in the bulk medium was investigated in a set of separate experiments using an Atomic Force Microscope (AFM). The latter work was undertaken at the University of Maine, USA and was aimed at determining the mutual effects that liquid bridge adhesion forces and the DLVO forces have on the mechanism of particle agglomeration in a liquid medium. In addition a feasibility study was conducted on behalf of a pharmaceutical company, Merck, Sharp and Dohme Ltd., to determine whether the MFB technique (in the gaseous bulk medium) could identify differences in behaviour between a paracetamol crystal and two different polymeric binders. Fresh paracetamol crystals were contacted and retracted from the two binders and differences in the amount of the liquid binder retained by the crystal were observed, which can be correlated to the mechanism of liquid binder distribution among particles during the process mixing. The success of the study demonstrated the flexibility and usefulness of the MFB approach to the investigation of real systems

Advanced Machine Learning Techniques for Condition Monitoring in Industrial Engineering Applications

Nella tesi sono stati affrontati diversi casi di studio provenienti da contesti industriali e per ognuno sono stati selezionati gli strumenti di Machine Learning più appropriati, con lo scopo di sviluppare soluzioni efficaci per il problema del monitoraggio delle condizioni. Le soluzioni proposte vogliono rappresentare un miglioramento rispetto a quelle attualmente impiegate nelle procedure industriali applicando tecniche di Machine Learning in nuovi contesti industriali. In primo luogo, è stato sviluppato un nuovo sistema per il monitoraggio delle condizioni operative dei bruciatori alimentati a carbone polverizzato impiegati in una centrale elettrica a carbone allo scopo di determinare se l'intero sistema di combustione funzioni correttamente nelle condizioni più efficienti dal punto di vista energetico. Questa parte del lavoro inizia con lo studio delle tecniche di Machine Learning per fornire una soluzione per la stima non invasiva delle dimensioni delle particelle di polvere di carbone. La stima delle dimensioni delle particelle costituisce il fondamento del metodo proposto. Ulteriori ricerche hanno sfruttato dati eterogenei provenienti da applicazioni simili ma non uguali, offrendo così una modellazione più accurata. Con l'obiettivo di migliorare l'implementazione del metodo riducendo il numero di osservazioni necessarie, il problema della stima delle dimensioni delle particelle è stato rivisto sotto forma di un problema di classificazione, pertanto gli algoritmi di Machine Learning precedentemente implementati sono stati opportunamente modificati e adattati per risolvere un problema di classificazione. Infine, gli algoritmi basati sulla classificazione sono stati utilizzati per sviluppare un sistema di monitoraggio completo. Il rilevamento dei guasti per i cuscinetti è un altro problema industriale rilevante che è stato risolto utilizzando un approccio di apprendimento automatico simile al caso precedente. In particolare, è stato sviluppato un classificatore basato su Support Vector Machine per rilevare e classificare diversi tipi di guasti. Dati sperimentali reali hanno consentito lo sviluppo di un sistema di rilevamento guasti che sfrutta l'analisi tempo-frequenza e l'analisi dell'immagine, i risultati ottenuti hanno dimostrato la sua idoneità sia per le condizioni operative stazionarie che non stazionarie dei cuscinetti. L'ultimo caso di studio proposto è un nuovo metodo non supervisionato per rilevare i guasti nei motori elettrici DC durante il controllo qualità alla fine della linea di produzione. Lo schema proposto utilizza un approccio di Novelty Detection consistente nell'utilizzo di una rete Denoising Autoencoder per modellare la condizione di normalità dei motori senza guasti, allo scopo di distinguere da questo background i motori con guasti che non rispettano i requisiti di qualità. I risultati ottenuti con test su dati sperimentali dimostrano la validità delle soluzioni sviluppate per ciascun case study affrontato, applicabili per l'implementazione di sistemi di monitoraggio per processi e dispositivi industriali dedicati al controllo di qualità e alla manutenzione delle apparecchiature.In the thesis, different case studies coming from industrial contexts have been addressed and for each one the most appropriate Machine Learning tools have been selected, with the purpose of developing effective solutions for the specific condition monitoring problem. The proposed solutions want to represent an improvement from those currently employed in the industrial procedure by applying Machine Learning techniques in new industrial contexts. Firstly, a new system for monitoring the operating conditions of pulverized coal burners employed in a coal fired power plant is developed with the aim to determine if the whole combustion system is working properly under the most energy efficient conditions. This part of the work begins with the investigation of Machine Learning techniques to provide a solution for non-invasive estimation of the coal powder particle size. The particle size estimation forms the foundation of the proposed method. Further research exploited heterogeneous data from similar but not same application, thus delivering more accurate modelling. With the aim of improving the implementability of the method by reducing the number of observations needed, the problem of particle size estimation has been reviewed in the form of a classification problem, therefore the previously implemented Machine Learning algorithms have been appropriately modified and adapted to solve a classification problem. Finally, the classification-based algorithms have been used to develop a complete monitoring system. Fault detection for roller bearings is another industrial relevant problem that has been tried to solve using a machine learning approach similar to the previous case. In particular a classifier based on Support Vector Machine has been developed to detect and classify different types of faults. Real experimental data has allowed development of a fault detection system that exploits the time-frequency analysis and image analysis, and the obtained results proved its suitability for both stationary and non-stationary operating conditions of roller bearings. The last case study proposed is a novel unsupervised method to detect faults in DC electric motors during quality control at the end of the production line. The proposed scheme uses a novelty detection approach consisting in using of a Denoising Autoencoder network for modelling the normality condition of the motors without faults, with the purpose of distinguish from this background the motors with faults that do not respect the quality requirements. The results obtained with tests on experimental data demonstrate the validity of the solutions developed for each addressed case study, applicable for the implementation of monitoring systems for industrial processes and devices devoted to quality check and equipment maintenance

Machine learning techniques for the estimation of particle size distribution in industrial plants

This paper aims to evaluate the effectiveness of different Machine Learning algorithms for the estimation of Particle Size Distribution (PSD) of powder by means of Acoustic Emissions (AE). In industrial plants it is very useful to use non-invasive and adaptable systems for monitoring the particle size, for this reason the AE represents an important mean for detecting the particle size. To create a model that relates the AE with the powder size, Machine Learning is a viable approach to model a complex system without knowing all the variables in details. The test results show a good estimation accuracy for the various Machine Learning algorithms employed in this study

Exploiting Heterogeneous Data for the Estimation of Particles Size Distribution in Industrial Plant

In industrial environments, it is often difficult and expensive to collect a good amount of data to adequately train expert systems for regression purposes. Therefore the usage of already available data, related to environments showing similar characteristics, could represent an effective approach to find a good balance between regression performance and the amount of data to gather for training. In this paper, the authors propose two alternative strategies for improving the regression performance by using heterogeneous data, i.e. data coming from diverse environments with respect to the one taken as reference for testing. These strategies are based on a standard machine learning algorithm, i.e. the Artificial Neural Network (ANN). The employed data came from measurements in industrial plants for energy production through the combustion of coal powder. The powder is transported in air within ducts and its size is detected by means of Acoustic Emissions (AE) produced by the impact of powder on the inner surface of the duct. The estimation of powder size distribution from AE signals is the task addressed in this work. Computer simulations show how the proposed strategies achieve a relevant improvement of regression performance with respect to the standard approach, using ANN directly on the dataset related to the reference plant

Unsupervised electric motor fault detection by using deep autoencoders

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors ʼ knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract Log-Mel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multi-layer perceptron ( MLP ) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory ( LSTM ) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine ( OC-SVM ) algorithm. The performance has been evaluated in terms area under curve ( AUC ) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OC-SVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %