A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiolog

Digital Image-Based Frameworks for Monitoring and Controlling of Particulate Systems

Particulate processes have been widely involved in various industries and most products in the chemical industry today are manufactured as particulates. Previous research and practise illustrate that the final product quality can be influenced by particle properties such as size and shape which are related to operating conditions. Online characterization of these particles is an important step for maintaining desired product quality in particulate processes. Image-based characterization method for the purpose of monitoring and control particulate processes is very promising and attractive. The development of a digital image-based framework, in the context of this research, can be envisioned in two parts. One is performing image analysis and designing advanced algorithms for segmentation and texture analysis. The other is formulating and implementing modern predictive tools to establish the correlations between the texture features and the particle characteristics. According to the extent of touching and overlapping between particles in images, two image analysis methods were developed and tested. For slight touching problems, image segmentation algorithms were developed by introducing Wavelet Transform de-noising and Fuzzy C-means Clustering detecting the touching regions, and by adopting the intensity and geometry characteristics of touching areas. Since individual particles can be identified through image segmentation, particle number, particle equivalent diameter, and size distribution were used as the features. For severe touching and overlapping problems, texture analysis was carried out through the estimation of wavelet energy signature and fractal dimension based on wavelet decomposition on the objects. Predictive models for monitoring and control for particulate processes were formulated and implemented. Building on the feature extraction properties of the wavelet decomposition, a projection technique such as principal component analysis (PCA) was used to detect off-specification conditions which generate particle mean size deviates the target value. Furthermore, linear and nonlinear predictive models based on partial least squares (PLS) and artificial neural networks (ANN) were formulated, implemented and tested on an experimental facility to predict particle characteristics (mean size and standard deviation) from the image texture analysis

Neuro-Fuzzy Based Intelligent Approaches to Nonlinear System Identification and Forecasting

Nearly three decades back nonlinear system identification consisted of several ad-hoc approaches, which were restricted to a very limited class of systems. However, with the advent of the various soft computing methodologies like neural networks and the fuzzy logic combined with optimization techniques, a wider class of systems can be handled at present. Complex systems may be of diverse characteristics and nature. These systems may be linear or nonlinear, continuous or discrete, time varying or time invariant, static or dynamic, short term or long term, central or distributed, predictable or unpredictable, ill or well defined. Neurofuzzy hybrid modelling approaches have been developed as an ideal technique for utilising linguistic values and numerical data. This Thesis is focused on the development of advanced neurofuzzy modelling architectures and their application to real case studies. Three potential requirements have been identified as desirable characteristics for such design: A model needs to have minimum number of rules; a model needs to be generic acting either as Multi-Input-Single-Output (MISO) or Multi-Input-Multi-Output (MIMO) identification model; a model needs to have a versatile nonlinear membership function. Initially, a MIMO Adaptive Fuzzy Logic System (AFLS) model which incorporates a prototype defuzzification scheme, while utilising an efficient, compared to the Takagi–Sugeno–Kang (TSK) based systems, fuzzification layer has been developed for the detection of meat spoilage using Fourier transform infrared (FTIR) spectroscopy. The identification strategy involved not only the classification of beef fillet samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also the simultaneous prediction of their associated microbiological population directly from FTIR spectra. In the case of AFLS, the number of memberships for each input variable was directly associated to the number of rules, hence, the “curse of dimensionality” problem was significantly reduced. Results confirmed the advantage of the proposed scheme against Adaptive Neurofuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) and Partial Least Squares (PLS) techniques used in the same case study. In the case of MISO systems, the TSK based structure, has been utilized in many neurofuzzy systems, like ANFIS. At the next stage of research, an Adaptive Fuzzy Inference Neural Network (AFINN) has been developed for the monitoring the spoilage of minced beef utilising multispectral imaging information. This model, which follows the TSK structure, incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. In this specific case study, AFINN model was also able to predict for the first time in the literature, the beef’s temperature directly from imaging information. Results again proved the superiority of the adopted model. By extending the line of research and adopting specific design concepts from the previous case studies, the Asymmetric Gaussian Fuzzy Inference Neural Network (AGFINN) architecture has been developed. This architecture has been designed based on the above design principles. A clustering preprocessing scheme has been applied to minimise the number of fuzzy rules. AGFINN incorporates features from the AFLS concept, by having the same number of rules as well as fuzzy memberships. In spite of the extensive use of the standard symmetric Gaussian membership functions, AGFINN utilizes an asymmetric function acting as input linguistic node. Since the asymmetric Gaussian membership function’s variability and flexibility are higher than the traditional one, it can partition the input space more effectively. AGFINN can be built either as an MISO or as an MIMO system. In the MISO case, a TSK defuzzification scheme has been implemented, while two different learning algorithms have been implemented. AGFINN has been tested on real datasets related to electricity price forecasting for the ISO New England Power Distribution System. Its performance was compared against a number of alternative models, including ANFIS, AFLS, MLP and Wavelet Neural Network (WNN), and proved to be superior. The concept of asymmetric functions proved to be a valid hypothesis and certainly it can find application to other architectures, such as in Fuzzy Wavelet Neural Network models, by designing a suitable flexible wavelet membership function. AGFINN’s MIMO characteristics also make the proposed architecture suitable for a larger range of applications/problems

Surface Defect Detection And Polishing Parameter Optimization Using Image Processing For G3141 Cold Rolled Steel

Traditionally the surface quality inspection especially for metal polishing purpose is perform by human inspectors. Defect detection is a method of nondestructive testing of material and products to detect defects. This study consists of two parts where the first part is applying vision system to detect and measure surface defects that have been characterized to some level of surface roughness. Specimen of G3141 cold rolled steel is used in this research as it represent the actual material applied in local automotive manufacturer. Gray image of scratch defect on metal surface is detected and information about mean gray pixel value (Ga) is interpreted and converted to surface roughness (Ra) measurement. In this study a new technique is developed where the Ga only read on the specific scratch line without considering the whole image. To realize this, automatic cropping algorithm is developed to detect the region of interest and interpret the Ga value. This techniques will enables the polishing to be done at specific scratch defect area without necessary to develop polishing path throughout the whole surface which is time consuming. Second part is to obtain the optimum polishing parameter by using artificial intelligence technique which is able to predict the grit size, polishing time and polishing force parameter to remove the scratch by polishing process. For the purpose of this study, multiple ANFIS or MANFIS have been selected to predict optimum parameter for polishing parameters. Polishing parameter data can be generated by using MANFIS to predict optimum polishing parameters such as grit size, polishing time and polishing force in order to perform polishing process. However due to lack of study done in the field of flat and dry polishing, the polishing parameter data have to be developed. The polishing parameter data for flat and dry polishing is performed by using robotic polishing arm and the experiment runs design by using full factorial design. Results show that the defect detection algorithm able to detect defect only on the scratch area and able to read the Ga value at detected scratch line and transform it to surface roughness measurement at considerably good level of accuracy compared with manual method. Results from MANFIS have shown that the system is able to predict up to 95% accuracy which is considerably high. The overall results from both parts of this research would inspire further advancements to achieve robust machine vision based surface measurement systems for industrial robotic processes specifically in polishing process

State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors

Producción CientíficaDespite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies