Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations.

International audienceIn this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD).Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive resonance theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it’s validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

The purpose of this study is to highlight approaches for predicting a system’s future behavior and estimating its remaining useful life (RUL) to define an effective maintenance schedule. Indeed, prognosis and health management (PHM) strategies for renewable energy systems, with a focus on wind turbine generators, are given, as well as publications published in the recent ten years. As a result, some prognostic applications in renewable energy systems are emphasized, such as power converter devices, battery capacity degradation, and damage in wind turbine high-speed shaft bearings. The paper not only focuses on the methodologies adopted during the early research in the area of PHM but also investigates more current challenges and trends in this domai

Prevalence of gastrointestinal parasitism infections in cattle of Bass Kabylie area: Case of Bejaia province, Algeria

The objective of this study was to determine the prevalence, identification of species and the dynamics of gastrointestinal (GI) parasites during humid and dry seasons in local cattle of different ages. The study was carried out in the Province of Bejaia, Algeria from December 2013 to June 2014. A total of 143 fecal samples were collected from different cattle herds. Fecal samples were visually examined then observed using flotation and sedimentation microscopic techniques. Eggs and worms were identified according to standard procedures. 63% of the cattle examined were found positive with one or more parasite species. Our results revealed that the eggs of Eimeria spp. are predominant (43.87%) followed by Strongylus spp. (30.32%) and Fasciola hepatica (12.25%). Eggs of Strongyloides papillosus, Moniezia benedeni, Paramphistomum daubneyi. and Toxocara vitulorum represent 1.29%, 1.93%, 1.93% and 6.45%, respectively. There is a significant difference between the sex of the animal and the prevalence rate of Strongyle spp. and Eimeria spp. (P< 0.01). As for the body condition score, there is a statically significant (P< 0.01) difference between the prevalence rate of GI parasite and the nutritional status of cattle. In conclusion, our preliminary investigation demonstrated highly prevalent and that abundance of the polyparasitism nature of the disease in Bass Kabylie area. Also, there was a relationship between the distribution of GI parasitism in cattle and the factors analyzed (body condition score, age and sex). Further studies are need for planning future research and to design rational and sustainable locally GI parasites control programmes

A New Data-Driven Approach for Power IGBT Remaining Useful Life Estimation Based On Feature Reduction Technique and Neural Network

The insulated gate bipolar transistor (IGBT) is a crucial component of power converters (PCVs) and is commonly used in several PCVs topologies. On the other hand, the investigation and the study of the IGBT component show several changes within its behavior and lifetime, while this component is highly influenced by the operating conditions. Indeed, the monitoring of this component is necessary to minimize unexpected downtime of the wind energy system (WES). However, an accurate prediction of IGBTs remaining useful life (RUL) is the key enabler for life-time-optimized operation. Consequently, this work proposes a new prognostic approach for online IGBTs monitoring that adopts the time-domain analysis to extract useful information that is used as an input in the generation of the health indicator. Moreover, this approach is based on combining both of principal component analysis (PCA) technique and the feedforward neural network (FFNN) technique. PCA is used to reduce features extracted from IGBTs and the FFNN is implemented to achieve online regression of the trend parameter obtained from the PCA technique. To investigate and evaluate the performance of our idea we used the NASA Ames Laboratory Prognostics Center of Excellence IGBTs accelerated aging database. Finally, the achieved results clearly show the strength of the new trend parameter for IGBTs RUL prediction. The most notable strong correlation within the proposed approach is in relation to accuracy value, with an acceptable average accuracy rate of 60.4%

Diagnosis of Xeroderma Pigmentosum Groups A and C by Detection of Two Prevalent Mutations in West Algerian Population: A Rapid Genotyping Tool for the Frequent XPC Mutation c.1643_1644delTG

Xeroderma pigmentosum (XP) is a rare autosomal recessive disorder. Considering that XP patients have a defect of the nucleotide excision repair (NER) pathway which enables them to repair DNA damage caused by UV light, they have an increased risk of developing skin and eyes cancers. In the present study, we investigated the involvement of the prevalent XPA and XPC genes mutations—nonsense mutation (c.682C>T, p.Arg228X) and a two-base-pair (2 bp) deletion (c.1643_1644delTG or p.Val548Ala fsX25), respectively—in 19 index cases from 19 unrelated families in the West of Algeria. For the genetic diagnosis of XPA gene, we proceeded to PCR-RFLP. For the XPC gene, we validated a routine analysis which includes a specific amplification of a short region surrounding the 2 bp deletion using a fluorescent primer and fragment sizing (GeneScan size) on a sequencing gel. Among the 19 index cases, there were 17 homozygous patients for the 2 bp deletion in the XPC gene and 2 homozygous patients carrying the nonsense XPA mutation. Finally, XPC appears to be the major disease-causing gene concerning xeroderma pigmentosum in North Africa. The use of fragment sizing is the simplest method to analyze this 2 bp deletion for the DNA samples coming from countries where the mutation c.1643_1644delTG of XPC gene is prevalent

Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

International audienceAccurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets