Comparative analysis of Hilbert Huang and discrete wavelet transform in processing of signals obtained from the cutting process: An intermittent turning example

Obrada nestacionarnih signala predstavlja jedan od izazova u proučavanju i upravljanju dinamičkih procesa. Najčešće korišćena kratkotrajna Furijeova transformacija i skalogram imaju značajne nedostatke u obradi signala prikupljenih iz dinamičkih sistema. Dve, relativno nove tehnike koje imaju značajno pogodnija svojstva u obradi nestacionarnih signala su Hilbert Huangova transformacija (Hilbert Huang Transform - HHT) i diskretna vejvlet transformacija (Discrete Wavelet Transform - DWT). Proces rezanja predstavlja izrazito dinamičan proces na koji utiču mnoge pojave kao što su proces formiranja strugotine i dinamički odzivi i stanje svih elemenata obradnog sistema. U ovom radu se vrši komparativna analiza HHT i DWT sa namerom da se ukazivanjem na njihova svojstva daju smernice za odluku koja od ove dve tehnike predstavlja tehniku izbora za konkretnu aplikaciju u skladu sa željenim rezultatima analize signala. Pored kratkih matematičkih osnova razmatranih transformacija, u radu se daju dva primera: prvi je numerički, a drugi, eksperimentalni, se odnosi na detekciju prekida procesa rezanja tokom prekidnog struganja.Nonstationary signal analysis is one of the greatest challenges in studying and control of dynamical processes. Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and conditions of machining system elements. The most commonly used Short Time Fourier Transform and spectrogram as its result have significant shortcomings in processing of signals acquired from dynamical systems. Two relatively new techniques that have notably better properties in the analysis of nonstationary signals are Hilbert Huang Transform (HHT) and Discrete Wavelet Transform (DWT). This paper gives comparative survey of HHT and DWT with the intention of giving the guidelines for deciding which of these techniques is the technique of choice for the analysis of signals obtained from cutting process, considering the desired outcomes of the analysis. Besides brief mathematical foundations of the transforms, the paper illustrates their utilization using two examples, one is numerical, and the other is experimental dealing with detection of cutting process stop during intermittent turning

Recognition of one class of surfaces from structured point cloud

U određenim oblastima industrije postoji potreba za generisanjem kompjuterskih modela objekata samo na osnovu njihove fizičke realizacije, a bez unapred poznatih konstrukcionih ili tehnoloških informacija. Pri realizaciji ovakvih zahteva istaknuto mesto zauzimaju tzv. tehnike reverznog inženjerstva geometrijskih modela. Bitnu fazu primene navedenih tehnika predstavlja prepoznavanje geometrijskih primitiva od kojih se posmatrani objekat sastoji. U ovom radu predstavljen je metod za segmentaciju i prepoznavanje G1 kontinualnih površina koje su u skenirnim linijama struktuiranog oblaka predstavljene eliptičnim segmentima. Predloženi algoritam je pre svega namenjen za prepoznavanje eliptičkih cilindara, elipsoida i eliptičkih torusa, ali se u zavisnosti od načina skeniranja dela, može koristiti i za prepoznavanje još nekih površi drugog reda. Proces segmentacije je zasnovan na prepoznavanju eliptičkih segmenata u skeniranim linijama, a na osnovu osobina singulariteta informacione matrice pri regresionoj analizi metodom najmanjih kvadrata. Verifikacija predloženog metoda je izvršena procesiranjem tri sintetizovana, kao i jednog realnog oblaka tačaka.This paper presents a method for recognition of surfaces represented by elliptical segments in structured three dimensional (3D) point clouds. The method is based on direct least squares fitting of ellipses in scanned lines. By recognizing elliptical segments in both directions of structured cloud it is possible to efficiently allocate G1 (and higher) continuous regions which represent a certain class of surfaces. The proposed method is primarily developed for recognition of elliptical cylinders and ellipsoids, including cylinders and spheres. Depending on scanning mode, the method can be employed for recognition of other second degree surfaces like cones. Besides, as presented in the paper, the method can be utilized for recognition of certain class of higher degree surfaces such as elliptical tori. The proposed method is experimentally verified using several synthesized point clouds as well as using a real world case study

A new approach to rubberized cord surface structure identification based on high-resolution laser scanning and multiresolution signal processing

Gumiranje čeličnog i tekstilnog korda predstavlja jednu od ključnih tehnologija procesiranja gume u proizvodnji auto guma. Tokom ovog procesa koji se izvodi na kalandrima izuzetno je teško ostvariti i držati konstantnom debljinu platna. Najnoviji razvoj optoelektronike i izvedenih senzorskih sistema omogućio je da se tradicionalni radioaktivni sistemi za merenje debljine gumiranog korda na linijama za kalandriranje zamene laserskim proksimetrima. Pored aposolutne bezbednosti u radu, laserski proksimetri poseduju vrhunsku tačnost, veliku brzinu uzorkovanja i ekstremno malu prostornu rezoluciju. Ova svojstva omogućuju gradnju mernih stanica, koje pored merenja debljine, omogućuju skeniranje poprečnog preseka obezbeđujući na taj način informacije o hrapavosti, teksturi i sveukupnoj valovitosti skeniranog profila. Informacija o valovitosti i teksturi može se iskoristiti za identifikaciju tehnoloških parametara režima procesa kalandriranja, što omogućava njegovo upravljanje i optimizaciju u realnom vremenu.Steel and textile cord coating is one of the key rubber processing technologies in tire making industry. It is carried out on calendaring lines where thickness variation across the sheet profile and downstream is very difficult to fulfill. Recent development of optoelectronics and derived sensory systems has enabled the replacement of traditional radioactive systems for measurement of calendared rubber thickness at calendaring lines by laser proximeters. Besides absolute work safety, laser proximeters have extreme accuracy, high sampling rate and small spatial resolution. These properties enable development of measuring systems, which besides thickness measurement enable lateral section scanning thus giving information on scanned profile roughness, overall waviness and texture. Information about profile waviness and texture can be used for identification of calendaring process parameters, which enable real-time control and optimization of this process. This paper gives a new conceptual approach for identification of surface structure calendaring process parameters based on multiresolution analysis of scanned lateral profile of rubberized cord

Time Localization of Abrupt Changes in Cutting Process using Hilbert Huang Transform

Cutting process is extremely dynamical process influenced by different phenomena such as chip formation, dynamical responses and condition of machining system elements. Different phenomena in cutting zone have signatures in different frequency bands in signal acquired during process monitoring. The time localization of signal’s frequency content is very important. An emerging technique for simultaneous analysis of the signal in time and frequency domain that can be used for time localization of frequency is Hilbert Huang Transform (HHT). It is based on empirical mode decomposition (EMD) of the signal into intrinsic mode functions (IMFs) as simple oscillatory modes. IMFs obtained using EMD can be processed using Hilbert Transform and instantaneous frequency of the signal can be computed. This paper gives a methodology for time localization of cutting process stop during intermittent turning. Cutting process stop leads to abrupt changes in acquired signal correlated to certain frequency band. The frequency band related to abrupt changes is localized in time using HHT. The potentials and limitations of HHT application in machining process monitoring are shown

Point Cloud Reduction Using Support Vector Machines

This paper explores the possibilities of point cloud reduction using \epsilon insensitive support vector regression (\epsilon-SVR). \epsilon-SVR is a technique that can carry out the regression using different kernel functions (sigmoid, radial basis function, B-spline, spline, etc.) and it is suitable for detection of flat regions and regions with high curvature in scanned data. Using \epsilon-SVR the density of preserved points is adaptive – preserved points are denser at highly curved region and rare at flat regions. Adjusting the error cost in the regression, the number of preserved points can be fine tuned

Poređenje Hilbert Huangove i diskretne vejvlet transformacije u analizi nestacionarnih signala: primena u prekidnom struganju

Obrada nestacionarnih signala predstavlja jedan od izazova u proučavanju i upravljanju dinamičkih procesa kakav je i proces rezanja. Najčešće primenjivana kratkotrajna Furijeova transformacija (STFT) i skalogram koji nastaje kao rezultat STFT imaju značajna ograničenja u obradi signala prikupljenih iz dinamičkih sistema. Dve relativno nove tehnike koje pokazuju značajno bolja svojstva u obradi nestacionarnih signala su Hilbert Huangova transformacija (HHT) i diskretna vejvlet transformacija (DWT). U ovom radu se vrši komparativna analiza HHT i DWT kako bi se razmatranjem njihovih prednosti i mana došlo do smernica za odlučivanje koja od ovih tehnika predstavlja tehniku izbora za analizu signala dobijenih iz procesa rezanja u zavisnosti od željenih rezultata analize. Pored kratkih matematičkih osnova ovih transformacija, u radu se daju i dva primera, jedan numerički, a drugi eksperimentalni koji se odnosi na prepoznavanje prekida procesa rezanja u prekidnom struganju

Reliable Industrial IoT-Based Distributed Automation

Reconfigurable manufacturing systems supported by Industrial Internet-of-Things (IIoT) are modular and easily integrable, promoting efficient system/component reconfigurations with minimal downtime. Industrial systems are commonly based on sequential controllers described with Control Interpreted Petri Nets (CIPNs). Existing design methodologies to distribute centralized automation/control tasks focus on maintaining functional properties of the system during the process, while disregarding failures that may occur during execution (e. g., communication packet drops, sensing or actuation failures). Consequently, in this work, we provide a missing link for reliable IIoT-based distributed automation. We introduce a method to transform distributed control models based on CIPNs into Stochastic Reward Nets that enable integration of realistic fault models (e. g., probabilistic link models). We show how to specify desired system properties to enable verification under the adopted communication/fault models, both at design-and run-time; we also show feasibility of runtime verification on the edge, with a continuously updated system model. Our approach is used on real industrial systems, resulting in modifications of local controllers to guarantee reliable system operation in realistic IIoT environments

The detection of sensor signal attacks in industrial control systems

U cilju povećanja produktivnosti i efikasnosti proizvodnje, četvrta industrijska revolucija vodi ka implementaciji kibernetsko fizičkih sistema i interneta stvari u industrijskom okruženju. Sveobuhvatna komunikacija čini kibernetsko fizičke sisteme podložnim na spoljašnje uticaje, koji često mogu imati negativnu nameru, npr. napadi i smetnje proistekli od različitih uzročnika. Uticaj napada na sistem može dovesti do anomalija i ozbiljnih posledica po delove sistema ili sistem u celosti. Stoga, odbrambeni mehanizmi za pravovremenu detekciju napada moraju biti razvijeni, kako bi se sistem zaštitio i održala njegova funkcionalnost. U ovom radu, predložen je metod za detekciju napada na senzorske signale u kontinualno upravljanim sistemima. Metod je baziran na mašinama sa nosećim vektorima, a testiran na skupu podataka iz sistema za preradu vode.To improve productivity and efficiency in industrial manufacturing, the fourth industrial revolution leads to the implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in the industrial environment. Ubiquitous communication makes CPS susceptible to external influences, which can have a negative intention; for instance, CPS are prone to various attacks and malicious threats by different adversaries. The impact of an attack on the system can lead to anomalies and serious consequences for system parts or the system as a whole. Security mechanisms must be developed in order to timely detect different attacks and to keep the system safe and protected. In this paper, a method for sensor signal attacks detection in a continuous time controlled systems has been proposed. The method is based on Support Vector Machines (SVM) and tested on the data obtained from the Secure Water Treatment (SWaT) testbed, a scaled-down plant that produces purified water

Recognition of Planar Segments in Point Cloud Based on Wavelet Transform

Within industrial automation systems, three-dimensional (3-D) vision provides very useful feedback information in autonomous operation of various manufacturing equipment (e.g., industrial robots, material handling devices, assembly systems, and machine tools). The hardware performance in contemporary 3-D scanning devices is suitable for online utilization. However, the bottleneck is the lack of real-time algorithms for recognition of geometric primitives (e.g., planes and natural quadrics) from a scanned point cloud. One of the most important and the most frequent geometric primitive in various engineering tasks is plane. In this paper, we propose a new fast one-pass algorithm for recognition (segmentation and fitting) of planar segments from a point cloud. To effectively segment planar regions, we exploit the orthonormality of certain wavelets to polynomial function, as well as their sensitivity to abrupt changes. After segmentation of planar regions, we estimate the parameters of corresponding planes using standard fitting procedures. For point cloud structuring, a z-buffer algorithm with mesh triangles representation in barycentric coordinates is employed. The proposed recognition method is tested and experimentally validated in several real-world case studies

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on epsilon insensitive support vector regression (epsilon-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of epsilon-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines