Predictive condition monitoring of industrial systems for improved maintenance and operation

Maintenance strategies based on condition monitoring of the different machines and devices in an industrial process can minimize downtime, increase the safety of plant operations and help in the process of decision-taking for control and maintenance actions in order to reduce maintenance and operating costs. Multivariate statistical methods are widely used for process condition monitoring in modern industrial sites due to the quantity of data available and the difficulties of building analytical models in complex facilities. Nevertheless, the performance of these methodologies is still far away from being ideal, due to different issues such as process nonlinearities or varying operational conditions. In addition application of the latest approaches developed for process monitoring is not widely extended in real industry. The aim of this investigation is to develop new and improve existing methodologies for predictive condition monitoring through the use of multivariate statistical methods. The research focuses on demonstrating the applicability of multivariate algorithms in real complex cases, the improvement of these methods in terms of fault detection and diagnosis by means of data fusion and the estimation of process performance degradation caused by faults.Marie Curi

Use of spectral kurtosis for improving signal to noise ratio of acoustic emission signal from defective bearings

The use of Acoustic Emission (AE) to monitor the condition of roller bearings in rotating machinery is growing in popularity. This investigation is centred on the application of Spectral Kurtosis (SK) as a denoising tool able to enhance the bearing fault features from an AE signal. This methodology was applied to AE signals acquired from an experimental investigation where different size defects were seeded on a roller bearing. The results suggest that the signal to noise ratio can be significantly improved using SK

Canonical variate analysis for performance degradation under faulty conditions

Condition monitoring of industrial processes can minimize maintenance and operating costs while increasing the process safety and enhancing the quality of the product. In order to achieve these goals it is necessary not only to detect and diagnose process faults, but also to react to them by scheduling the maintenance and production according to the condition of the process. The objective of this investigation is to test the capabilities of canonical variate analysis (CVA) to estimate performance degradation and predict the behavior of a system affected by faults. Process data was acquired from a large-scale experimental multiphase flow facility operated under changing operational conditions where process faults were seeded. The results suggest that CVA can be used effectively to evaluate how faults affect the process variables in comparison to normal operation. The method also predicted future process behavior after the appearance of faults, modeling the system using data collected during the early stages of degradation

Application of acoustic emission in diagnostic of bearing faults within a helicopter gearbox

AbstractAcoustics Emissions (AE) technology has emerged as a promising diagnostic approach. AE was originally developed for non-destructive testing of static structures, however, in recent times its application has been extended to health monitoring of rotating machines. This paper introduces a novel method for application of AE in monitoring of helicopter gearboxes. In addition this paper investigates the application of signal separation techniques in detection of bearing faults within the epicyclic module of a large helicopter (CS-29) main gearbox using Acoustic Emissions (AE). The results showed successful of AE in detection bearing fault within the helicopter gearbox. Detection of the small bearing defect gives the AE an indisputable diagnosis advantage and prove ability of application of AE in helicopter gearboxes

Incipient Fault Detection, Diagnosis, and Prognosis using Canonical Variate Dissimilarity Analysis

Industrial process monitoring deals with three main activities, namely, fault detection, fault diagnosis, and fault prognosis. Respectively, these activities seek to answer three questions: ‘Has a fault occurred?’, ‘Where did it occur and how large?’, and ‘How will it progress in the future?’ As opposed to abrupt faults, incipient faults are those that slowly develop in time, leading ultimately to process failure or an emergency situation. A recently developed multivariate statistical tool for early detection of incipient faults under varying operating conditions is the Canonical Variate Dissimilarity Analysis (CVDA). In CVDA, a dissimilarity-based statistical index was derived to improve the detection sensitivity upon the traditional canonical variate analysis (CVA) indices. This study aims to extend the CVDA detection framework towards diagnosis and prognosis of process conditions. For diagnosis, contribution maps are used to convey the magnitude and location of the incipient fault effects, as well as their evolution in time. For prognosis, CVA state-space prediction and Kalman filtering during faulty conditions are proposed in this work. By covering the three main process monitoring activities in one framework, our work can serve as a baseline strategy for future application to large process industries

Conditioned increase of locomotor activity induced by haloperidol

Dopamine antagonist drugs have profound effects on locomotor activity. In particular, the administration of the D2 antagonist haloperidol produces a state that is similar to catalepsy. In order to confirm whether the modulation of the dopaminergic activity produced by haloperidol was repeatedly paired with the presence of distinctive contextual cues that served as a Conditioned Stimulus. Paradoxically, the results revealed a dose-dependent increase n locomotor activity following conditioning with dopamine antagonist (Experiments 1) that was susceptible of extinction when the conditioned stimulus was presented repeatedly by itself after conditioning (Experiment 2). These data are interpreted from an associative perspective, considering them as a result of a classical conditioning process.Ministerio de Economía, Industria y Competitividad PSI2015-64 965-

Influence of air temperature on drying kinetics and antioxidant potential of olive pomace

This work aims to evaluate the influence of olive pomace drying (a solid by-product of the olive oil industry) on both antioxidant potential and drying kinetics. The two main fractions of olive pomace (pits, PI and pulps + peels, P + P) were characterized by image analysis and density measurement. The drying process was analyzed in experiments carried out at different temperatures (from 50 to 150 C) and mathematically described from the diffusion and Weibull models. The antioxidant potential of the extracts (ethanol water 80:20 v/v, 22 ± 1 C, 170 rpm for 24 h) obtained from the dry product was analyzed by measuring the total phenolic content and antioxidant capacity and the main polyphenols were quantified by HPLC DAD/MS MS. The drying behavior of olive pomace was well described by considering the diffusion in the PI and P + P fractions separately and the influence of temperature on effective moisture diffusivities was quantified by an Arrhenius type equation. The antioxidant potential was only mildly influenced by the drying temperature. However, long drying times at the highest temperature tested (150 C) significantly (p < 0.05) increased the antioxidant potentialThe authors acknowledge the Generalitat Valenciana (PROMETEO/2010/062 and PROMETEO/2012/007) and Ministerio de Economia y Competitividad (AGL2011-29857-C03-04) for their financial support and the Ministerio de Educacion, Cultura y Deporte of Spain for the financing through the Formacion de Profesorado Universitario del Programa Nacional de Formacion de Recursos Humanos de Investigacion.Ahmad-Qasem Mateo, MH.; Barrajón Catalán, E.; Micol, V.; Cárcel Carrión, JA.; García Pérez, JV. (2013). Influence of air temperature on drying kinetics and antioxidant potential of olive pomace. Journal of Food Engineering. 119(3):516-524. https://doi.org/10.1016/j.jfoodeng.2013.06.027S516524119

Bearing signal separation enhancement with application to helicopter transmission system

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Bearing vibration signal separation is essential for fault detection of gearboxes, especially where the vibration is nonstationary, susceptible to background noise, and subjected to an arduous transmission path from the source to the receiver. This paper presents a methodology for improving fault detection via a series of vibration signal processing techniques, including signal separation, synchronous averaging (SA), spectral kurtosis (SK), and envelope analysis. These techniques have been tested on experimentally obtained vibration data acquired from the transmission system of a CS-29 Category A helicopter gearbox operating under different bearing damage conditions. Results showed successful enhancement of bearing fault detection on the second planetary stage of the gearbo

A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox

Whilst vibration analysis of planetary gearbox faults is relatively well established, the application of Acoustic Emission (AE) to this field is still in its infancy. For planetary-type gearboxes it is more challenging to diagnose bearing faults due to the dynamically changing transmission paths which contribute to masking the vibration signature of interest. The present study is aimed to reduce the effect of background noise whilst extracting the fault feature from AE and vibration signatures. This has been achieved through developing of internal AE sensor for helicopter transmission system. In addition, series of signal processing procedure has been developed to improved detection of incipient damage. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were applied to AE and vibration data acquired from a simplified planetary gearbox test rig with a seeded bearing defect. The results show that AE identified the defect earlier than vibration analysis irrespective of the tortuous transmission pat