Condition Monitoring maintenance vibration analysis

The papers published in these proceedings are presented in the Third International Seminar on Maintenance, Condition Monitoring and Diagnostics, to be arranged in Oulu, Finland, in 29th – 30th September, 2010.

Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present seminar is supported by a variety of Finnish industrial enterprises.

ISBN 978-951-98113-5-2

Industrial condition monitoring is typically performed by experts working in a maintenance organisation. Especially in the process industry the roles of the maintenance personnel and operators are highly separated, which in many cases lead to communication discrepancies that can impair the efficiency of the condition monitoring programme. Concepts such as TPM and its close relative LEAN emphasise the need for operator involvement in maintenance (1, 2). Regardless of the efforts, the concepts of operator maintenance or Autonomous Maintenance (AM) have not become widely accepted in the process industry. By combining operators' daily machinery checks and condition monitoring, it is possible to create a natural communication channel between operators and condition monitoring experts for improved efficiency. The approach known as Operator Driven Reliability (ODR) utilises modern portable technology to enable operators to check and record machinery data as an integral part of their daily operator rounds (3). This includes visual machinery checks but also some relatively simple vibration and temperature measurements. The data are collected and stored in a database that can be accessed both by operators and the maintenance personnel. It is important to understand that technology is just an enabler for creating systematic and effective work processes. Therefore, it is vital that operator checks are carefully planned in order to support the effectiveness of production and maintenance.

One challenge for the development of accurate vibration-based diagnostics in U.S. Army Health and Usage Monitoring Systems (HUMS) is the fine-tuning of condition indicators (CIs) with limited ground truth data sets. The most successful diagnostics, regardless of method, are those based on a fleet-wide ground truth dataset from naturally occurring faults, which takes many years and significant investment to develop. During the development period, aircraft crews often have no prior indication of an impending bearing failure, creating the need for an interim solution. In response to this challenge, new techniques for the maturation of vibration-based bearing diagnostics have been developed. The U.S. Army Aviation Engineering Directorate's Bearing Resonance Project [1, 2] has been completed for Army HUMS-equipped rotorcraft, and the applications of the findings are presented herein. The technique utilized for the Bearing Resonance Project simulates a fault signature from each monitored bearing on the aircraft and measures the frequency response function (FRF) between the fault initiation site and the bearing's associated accelerometer. The FRF is then analyzed and frequency windows are suggested as new CIs. These new CIs are analyzed to determine their effectiveness using the existing ground truth data from the AH-64 platform. The AH-64 platform has the most comprehensive database of confirmed faulted bearings throughout its drive train. This paper will discuss the results, with emphasis on the procedures and methodology followed to obtain these results.

Cooling Tower Fan ini mengalami getaran tinggi dan dari hasil analisa terindikasi adanya masalah pada blade nya. setelah cooling tower dibongkar, ditemukan masalahnya terjadi erosi pada blade cooling tower fan. cooling tower fan ini merupakan salah satu asset milik Chevron Geothermal Salak yang berfungsi untuk sistem pendingin uap dari proses steam turbine.

Condition Monitoring (CM) is an essential element of securing reliable operating conditions of Wind Turbines (WT) in a wind farm. CM helps optimize maintenance by providing Remaining Useful Life (RUL) forecast. However, the expected RUL is not often reliable due to uncertainty associated with the prediction horizon. In this paper, we employ high-level fusion methods to expect the RUL of WT bearings. For this purpose, various features are extracted by vibration signals to capture deterioration paths. Then, a Bayesian algorithm is utilized to determine RUL for each selected feature. Eventually, high-level fusion schemes, including Hurwicz, Choquet integral, Ordered Weighted Averaging operator, are employed to integrate RUL numbers and lessen associated uncertainty in the prediction horizons. Besides, a pessimistic fusion strategy is driven to obtain a bounded uncertainty for the worst RUL prediction. The fusion methods are assessed by ten-year vibration signals of Canadian wind farms.  Experimental results confirm accurate results with bounded uncertainty for high-level fusion approaches.

The basic engineering tradesmen and operators need to keep plant reliability up and operating costs down. From www.feedforward.com.au – The world best practice plant and equipment maintenance site. ABSTRACT Shaft alignment on pumps. Shaft misalignment is one of the most common reasons for bearing and mechanical seal failures. Rotating misaligned shafts produce vibration and complex fluctuating radial and axial loads that lead to breakdowns. Shaft alignment is a precision maintenance requirement that requires exacting care and detail and if not performed will cause much production down time.

Condition monitoring enables reliable and economical way of action for maintenance operations in modern industrial plants. Increasing number of measurement points and more demanding problems require automatic fault detection. Advanced signal processing methods expose failures earlier and provide information on suitable operating conditions of machines. Intelligent methods have been increasingly used in model based fault diagnosis and intelligent analysers. These methods provide various techniques for combining a large number of features.

Lime kilns are large machines, approximately 4 meters in diameter and even more than 100 meters long, with very slow rotational speeds. Depending on production conditions, the kiln must run at different production capacity and rotational speed. The speed is controlled together with the fuel feed and the draught fan speed to obtain good operating conditions. Temperatures in the hot end are very high.

Alignment problems of the kiln are severe because of the high weight affecting on the supporting rolls. Problems may lead to serious damages or even to fire. The situations are very bad for the whole production since lime kilns are essential parts of the chemical recovery cycle of a pulp mill. The chemical pulp production is strongly integrated, and a smooth operation is achieved if all the subprocesses are operating well. Condition monitoring is getting more and more important as the period of continuous operation of the pulp plant is planned to be increased to 18 months. Earlier there have been usually three maintenance shut-downs during a year.

In this paper, a large set of previously collected measurements was analysed with intelligent models based on new features. The acceleration measurements were done on the bearing housing of the supporting rolls. This 97.5 meter long kiln has eight supporting rolls with sleeve bearings, and there are two measurement points for each roll. The rotation time of the kiln is from 42 to 45 seconds, and a rotation of a supporting roll takes from 12.2 to 13.1 seconds.

Predictive Maintenance strategy employs vibration analysis, thermography analysis, ultrasound analysis, oil analysis and other techniques to improve machine reliability. The goal of the strategy is to provide the stated function of the facility, with the required reliability and availability at the lowest cost.

Ce travail présente un modèle théorique d’estimation de la durée de vie résiduelle utile des roulements à billes en s’appuyant sur l’analyse vibratoire. Les vibrations sont des phénomènes physiques utilisés pour mettre en évidence les défauts de fonctionnement ou de dégradation d’une machine tournante et donnent une meilleure mesure de son état. La détection de la détérioration précoce d’un roulement est mise en évidence par l’utilisation d’un indicateur de défaut. Les roulements présentent plusieurs stades de dégradation. Cette méthodologie sera basée sur deux étapes clés. D’abord, il sera question de détecter les défauts en procédant par l’analyse du spectre du signal. Puis nous allons estimer la durée de vie utile restante en utilisant des courbes d’ajustement exponentiel et les rapports des durées des phases de dégradation du roulement.
Mots-clés : roulement à billes, analyse vibratoire, vibration, machine tournante, indicateur de défaut, spectre, courbe d’ajustement.

To reduce visual and environmental impact, modern power stations are built with induced draft cooling towers replacing the large natural draft cooling towers previously used. The ability of the cooling towers to provide adequate cooling for the main circulating water of the power station is affected by the availability of the cooling tower fans.

Wind power has expanded significantly over the past
years, although reliability of wind turbine systems,
especially of offshore wind turbines, has been many
times unsatisfactory in the past. Wind turbine failures
are equivalent to crucial financial losses. Therefore,
creating and applying strategies that improve the
reliability of their components is important for a
successful implementation of such systems. Structural
health monitoring (SHM) addresses these problems
through the monitoring of parameters indicative
of the state of the structure examined. Condition
monitoring (CM), on the other hand, can be seen
as a specialized area of the SHM community that
aims at damage detection of, particularly, rotating
machinery. The paper is divided into two parts: in
the first part, advanced signal processing andmachine
learning methods are discussed for SHM and CM on
wind turbine gearbox and blade damage detection
examples. In the second part, an initial exploration
of supervisor control and data acquisition systems
data of an offshore wind farm is presented, and
data-driven approaches are proposed for detecting
abnormal behaviour of wind turbines. It is shown that
the advanced signal processing methods discussed are
effective and that it is important to adopt these SHM
strategies in the wind energy sector.

Advances in wind turbine power generation technology and legislative decisions of governments that are favorable to renewable power have lead wind energy to be an increasingly viable alternative to conventional power generation. However, because of the broadly variable loads and the aggressive operating conditions, wind turbines are subject to relatively high failure rates and their measurement signals reflecting the condition monitoring parameters are eminently variable and subject to large dynamic range. In this respect, keeping away from costly breakdowns and pursuing an operation with acceptable performance requires implementation of advanced condition monitoring and diagnostic systems to be developed specifically for wind turbines. Condition-based maintenance is recognized as the best method in maintenance strategies as its diagnostic and prognostic features allow avoiding catastrophic failures and schedule timely maintenance according to the estimations made on the remaining useful life. In this respect, methods for condition monitoring of wind turbines are surveyed comparing their diagnostic features. We conclude with a discussion on conceivable future trends and pertinent challenges and opportunities on condition monitoring of wind turbines.

This article provides a literature review on solar-powered Stirling engines technology. A
number of research works on the development of Stirling engines, solar-powered
Stirling engines have been discussed. The aim of this review is to not only find a feasible
solution which may lead to a preliminary conceptual design of a workable solar powered
Beta Stirling engine but also to present the output from different analytical
techniques (Schmidt and West formulate) and hence validate the use of either approach.
Results from the study indicate that Stirling engines working on solar energy are an
attractive alternative for the future. They are, by virtue, the most efficient response to
the existent energy crisis - and for states like Pakistan they provide a good solution.

Se pencher sur une machine pour « écouter » les bruits révélateurs de défauts ou contrôler visuellement l’apparition de fissures sur des pièces délicates représentent des opérations courantes. Pour s’affranchir de ces opérations fastidieuses et plus ou moins efficaces, les entreprises industrielles se tournent vers des systèmes de surveillance automatique de leurs machines. Ils leur permettent d’accroître la rentabilité (en diminuant au maximum le travail de l’opérateur), mais aussi de garantir la qualité des produits fabriqués et la sûreté de fonctionnement.
L’intérêt de ces systèmes est double:
Détecter une anomalie le plus tôt possible
Réaliser un diagnostic complet pour en identifier les causes.
En surveillant l’apparition de défauts critiques, on peut prévoir les pannes et en minimiser les conséquences, les réparations sont réalisées au moment opportun, elles sont moins coûteuses et les arrêts de production sont limités et surtout justifiés. On essaie ainsi d’anticiper les ruptures d’outil qui peuvent endommager la machine.
Cette approche moderne de maintenance consiste à mesurer l’état d’une machine pendant son fonctionnement afin de n’intervenir que lorsque les paramètres l’indiquent. Il s’agit de la maintenance prédictive. Dans le cadre de cette étude, nous nous appesantirons sur le paramètre qui s’avère être le plus fiable dans la mesure où il donne le plus précocement et de la meilleure façon l’état de détérioration d’une machine tournante. Il s’agit des vibrations.
Le présent travail énonce et analyse les principes de mise en oeuvre de la maintenance prédictive, relève son intérêt économique et technique, justifie son choix en regard de toutes les techniques de maintenance. Ils présentent les différentes méthodes utilisées pour la détection des défauts sur les machines industrielles par le contrôle des vibrations.
Pour atteindre les objectifs ci-dessus présentés, le présent travail est structuré selon le plan suivant :
o Le premier chapitre concerne les vibrations mécaniques, leurs évaluations dans le cadre de la maintenance prédictive, l’analyse de ceux-ci ainsi que les informations indispensables pour leurs traitements.
o Le deuxième chapitre présente les concepts nécessaires à la compréhension de la maintenance prédictive par rapport à l’intérêt économique et technique poursuivi parla maintenance des équipements industriels. L’intégration des vibrations dans le cadre de la maintenance prédictive étant mis en évidence.
o Le troisième chapitre concerne les méthodes de détection des défauts. Il présente les principales activités utilisées pour la détection des défauts des machines tournantes.
o Le quatrième chapitre traite des défauts et de leurs manifestations vibratoires. Il présente les principaux défauts observables sur les machines tournantes, leurs manifestations et leurs origines ainsi que le principe d’établissement des niveaux d’alarme et de pré alarme sur les machines.
o Le cinquième chapitre concerne la prédiction de l’amplitude vibratoire à l’aide du logiciel MATLAB. Il présente de quelle manière le diagnostic prédictif peut être réalisé en s’appuyant sur un programme de prédiction qui est présenté.

The papers published in these proceedings are presented in the International Conference on Maintenance, Condition Monitoring and Diagnostics, and Maintenance Performance Measurement and Management, MCMD 2015 and MPMM 2015, to be arranged in Oulu, Finland, in 30th September – 1st October, 2015.

Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present conference is supported by a variety of Finnish industrial enterprises.

ISBN 978-951-98113-7-6

Machine breakdown due to unplanned maintenance (sudden failure) will increase the repair cost and machine downtime (production lost). This has brought the function of maintenance to be an important activity in the manufacturing industry in order for daily operation to become stable. The introduction of several philosophies such as Corrective Maintenance (CM), Preventive Maintenance (PM) or Total Productive Maintenance (TPM) have allowed extra solutions to a process planning problem faced by company in comparison to the conventional fire-fighting syndrome. This main purpose of this study was to focus on developing a framework of maintenance strategy as guidelines for Mechanical Section in Engineering Department for Malaysian NPK Fertilizer Sdn. Bhd. (MNFSB) located in Gurun Industrial Estate, Kedah. From an extensive review of literature for Total Productive Maintenance (TPM), twelve disciplines beginning with basic to advanced elements were identified for effective Maintenance Management Systems. To demonstrate the validity of this study, it was tested by implementing it on actual equipment, namely as Bucket Conveyor, EL102 as a case study.

— Rolling element bearings comes under the critical category in many rotating machineries, mainly in chemical industry, aviation, nuclear power stations etc. Vibration monitoring and analysis is useful tool in the field of predictive maintenance. Health of rolling element bearings can be easily identified using vibration monitoring because vibration signature reveals important information about the fault development within them. Numbers of vibration analysis techniques are being used to diagnosis of rolling element bearings faults. This paper attempts to summarize the recent research and developments in rolling bearing vibration analysis techniques. Bearing defects and bearing characteristic frequencies (BCF) are also discussed.

The papers published in these proceedings are presented in the Second International Seminar on Maintenance, Condition Monitoring and Diagnostics, to be arranged in Oulu, Finland, in 28th – 29th September, 2005.

Arranged by the University of Oulu and POHTO – The Institute for Management and Technological Training, the present seminar is supported by a variety of Finnish industrial enterprises.

ISBN 951-98113-3-8

This work proposes a novel prototype-based engine fault classification scheme employing the audio signature of engines. In this scheme, Fourier transform and correlation methods have been used. Notably, automated audio classification has immense significance in the present times, used in both audio-based content retrieval and audio indexing in multimedia industry. Likewise, it is also becoming increasingly important in automobile industries. It has been observed that real world automobile engine audio data are contaminated with substantial noise and out fliers. Hence, it is challenging to categorize different fault types in different engines. Accordingly, the present paper discusses a methodology where a set of algorithms checks the state of an unknown engine as either healthy or faulty. Fault categorizing algorithm is based on its cross-and autocorrelation coefficient values. Appropriately, in this study, the engine amplitude-frequency values of fast Fourier transform are calculated and subdivided into bands to calculate the correlation coefficient matrix. The correlation coefficient matrix for the unknown engine is then calculated and matched with this "prototype" engine matrix to categorize it into a single or multiple fault(s). It is worth mentioning here that although a rank-based maximum close scheme is adopted for finding the unknown engine's fault, the work can be extended to any other parametric and neural network-based classification scheme. Keeping this background in mind, the present paper discusses the proposed methodology to find a prototype engine, unknown engine classification, implementation on real audio signal for single cylinder engine data, and its results.

Acoustics 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.

Stadiums like those used for sporting or concert events are distinct from other civil engineering structures due to several different characteristics. Some challenges mainly originate from the interaction with the human factor, as stadiums are subjected to both synchronized and random motion of large crowds. The investigations in the literature on this topic clearly state that stadiums designs are in urgent need of more reliable load quantification and modeling strategies, deeper understanding of structural response, generation of simple but efficient human–structure interaction models, and more accurate criteria for vibration acceptability. Although many esthetically pleasing and technologically advanced stadiums have been designed and constructed using structurally innovative methods, recent research on this field still calls for less conservative and more realistic designs. This article aims to highlight the recent advances in this field and to provide a follow-up to the literature review covering until 2008 (Jones et al., 2011a) on vibration serviceability of stadiums structures. The article will also discuss new sensing and monitoring techniques on load-time history measurements and their regeneration, as well as crowd motion, stadium health monitoring, and human comfort analysis. Operational effects of crowds on the dynamic properties are also discussed. The article concludes with a forward look on the recommended work and research for dynamic assessment of stadiums.

The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cutoff frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.

Cepstral filtering technique is applied on an interlaced image, the pattern similar to that which is found in layer IV of Primate Visual Cortex. It involves Power spectrum in computation, which is square of absolute of Fast Fourier Transform (FFT), is a complicated and hardware unfriendly. We propose an algorithm in which Gabor filters, instead of Power Spectrum, are applied to an interlaced image in the Cepstral algorithm. This scheme makes it hardware friendly as it gives the flexibility of working with modules which can be imitated in hardware. Building a FFT module is a tough task in analog circuit but determining Gabor energy, an alternative to it, can be achieved by elementary circuits. The Phase, Energy Models and other methods, use multi-lambda Gabor filters to compute disparity. The proposed method uses sum of absolute difference to choose a single Gabor filter of appropriate lambda that fits to find the disparity. The algorithm inherits the quality of both Gabor filter and Ocular Dominance Pattern and hence a biologically inspired and suitable for hardware realization. The proposed algorithm has been implemented on the test data image. A hardware scheme has also been proposed that can be used to estimate disparity and the idea can be extended in building complex modules that can perform real time-real image operations with a handful of resources as compared to employing complex digital FPGAs and CPLDs.

Wind turbines commonly undergo non-stationary flow and, not rarely, even rather extreme phenomena. In particular, rough terrains represent a challenging testing ground, because of the combination of terrain-driven flow and wakes. It is therefore crucial to assess the impact of dynamic loads on the turbines. In this work, tower and drive-train vibrations are analyzed, from a subcluster of four turbines of a wind farm sited in a very complex terrain. The main outcome of the study is that it is possible to start from the analysis of wind conditions and interpret how wakes manifest in the vibrations of the turbines, both at structural level (tower vibrations) and at the drive-train level. This wind to gear approach therefore allows to build a connection between a flow phenomenon and a mechanical phenomenon (vibrations) and can be precious to assess loads in different working conditions.

Advanced signal processing methods combined with automatic fault detection enable reliable condition monitoring for long periods of continuous operation. Root-mean-square and peak values obtained from vibration signals are useful features for detecting various faults. Unbalance, misalignment, bent shaft, mechanical looseness and some electrical faults, for example, can be detected using features of displacement and velocity. Higher order derivatives provide additional possibilities for detecting faults that introduce high-frequency vibrations or impacts. Real order derivatives increase the number of signal alternatives. New generalised moments and norms related to lp space have been used for diagnosing faults in a roller contact on a rough surface. Kurtosis provides a strong indication if the order of derivation, α, is at least 4. For peak values, the change is smaller but already starts at α = 3. The generalised moment and norm can be defined by the order of derivation, the order of the moment, p, and sample time, τ. Reliable results can be obtained by relative norms if α and p are in the range between 4 and 6. For the impact of a small scratch, sensitivity is further improved with short sample times, but several sequential samples are required to guarantee the detection of impacts. Then the order p can also be reduced.

—This paper experimentally investigates the type of tooth defects in a gear of gearbox system using neural networks with a astonishing high accuracy. Slight-worn, medium-worn, and broken-teeth of gears were considered as gears faults. In almost most of fault-diagnosis methods, vibration signals of a machine are predominantly used to detect mechanical faults. A new feature vector for training purposes of neural network is also presented using energy coefficients of wavelet packet from these signatures. Ultimately a MLP network was presented that offers not only an undersized configuration but also high performance giving it the capability to identify faults perfectly in 99% of the circumstances.

—Bearings are the most common mechanical components used in machinery to support rotating shafts. Due to harsh working conditions, bearing performance deteriorates over time. To prevent any unexpected machinery breakdowns caused by bearing failures, statistical modeling of bearing degradation signals should be immediately conducted. In this paper, given observations of a health indicator, a statistical model of bearing degradation signals is proposed to describe two distinct stages existing in bearing degradation. More specifically, statistical modeling of Stage I aims to detect the first change point caused by an early bearing defect, and then statistical modeling of Stage II aims to predict bearing remaining useful life. More importantly, an underlying assumption used in the early work of Gebraeel et al. is discovered and reported in this paper. The work of Gebraeel et al. is extended to a more general prognostic method. Simulation and experimental case studies are investigated to illustrate how the proposed model works. Comparisons with the statistical model proposed by Gebraeel et al. for bearing remaining useful life prediction are conducted to highlight the superiority of the proposed statistical model.