Radial basis and LVQ neural network algorithm for real time fault diagnosis of bottle filling plant

U ovom je radu razvijena umjetna neuronska mreža (ANN) za brzo pronalaženje grešaka na pneumatskom sustavu. Podaci su prikupljeni i procijenjeni smatrajući da sustav radi savršeno, a greške su unaprijed predviđene. Greške uključuju manjak boce, ne funkcioniranje cilindra B za stavljanje poklopca, neispravni cilindar C za stavljanje poklopca na boce, nedovoljan tlak zraka, voda se ne puni i nizak tlak zraka. Tijekom postupka prikupljeni su signali šest senzora te je za ANN kodirano 18 najkarakterističnijih obilježja podataka. Primijenjene su dvije različite umjetne neuronske mreže (ANN) za interpretaciju kodiranih signala. Umjetne neuronske mreže testirane u ispitivanju bile su "learning vector quantization (LVQ)" i "radial basis network (RBN)". Ustanovilo se da te dvije vrste umjetnih neuronskih mreža dobro funkcioniraju u primijenjenim postupcima u sustavu u kojem se sekvencijski podaci ponavljaju.In this study, an Artificial Neural Network (ANN) is developed to find faults rapidly on a pneumatic system. The data were saved and evaluated considering system is working perfectly and faults are predetermined. These faults include having no bottle, a nonworking cap closing cylinder B, a nonworking bottle cap closing cylinder C, insufficient air pressure, water not filling and low air pressure faults. The signals of six sensors were collected during the entire sequence and the 18 most descriptive features of the data were encoded to present to the ANNs. Two different ANNs were applied for interpretation of the encoded signals. The ANNs tested in the study were learning vector quantization (LVQ) and radial basis network (RBN). The performance of LVQ and RBN was found to be fine with the presented procedures for a system having very repetitive sequential data

Kendi enerjisini üretebilen klavye tasarımı

Üretimdeki gelişmeler, düşük güçlerde çalışan devre tasarımları ve ağ tekniklerini ortaya çıkarmıştır. Bu nedenle elektronik cihazların güç gereksinimleri azalmıştır. Enerji sistemlerindeki azalan güç ihtiyacı, pillere alternatif sistemlerin kullanılma olasılığı ile birlikte farklı aygıtların ortaya çıkmasına sebep olmuştur. Enerji ile çalışan aygıtlarda ortamdaki hareket enerjisinin yakalanması ve bu enerjinin kullanılabilir elektrik enerjisine dönüştürülmesi için piezo elektrik malzemeler kullanılması mümkündür. Bu çalışmada farklı klavyelere uygulanabilecek çeşitli genişlik ve uzunluklarda piezo elektrik malzemeler ve yaylar kullanılmıştır. Uygulanan kuvvete bağlı olarak piezo elektrik üzerinde oluşan gerilmelerle elektrik enerjisi üretilmiştir

Intelligent monitoring of linear stages with ensembles of improved LeNET DCNN and random forest classifiers

The linear stages are the most critical component of machine tools and additive manufacturing equipment. The accuracy of the linear stages directly affects the quality of the parts produced. Misalignment is a common problem in the linear stages. This paper presents a sensorless approach for detecting misalignment by monitoring the motor current. A linear stage was designed to simulate various angular misalignment problems between the ball screw and the motor shaft. The sensorless current-based method monitored the motor current at the Programmable Logic Controller (PLC) to detect the misalignment of the linear stage. Different forces were applied to the linear stage under different misalignment conditions. The acquired signal was processed using Continuous Wavelet Transform (CWT). The Lenet DCNN (Deep Convolutional Neural Network) model structure was improved by hyper-parametertuning and ensemble. The ensemble method combined the Convolutional Neural Network (CNN) model with a random forest (RF) classifier. The developed anomalydetection system was trained when different forces, with and without misalignment, were applied. The results showed that the proposed method was feasible for estimating the misalignment even when different external forces were applied to the linear stage

Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data

CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs