Condition based maintenance is becoming increasingly popular in many industrial contexts, offering substantial savings and minimising accidental damage. When applied to rotating machinery, its most common tool is vibration analysis, which relies on well-established mathematical models rooted in the theory of cyclo-non-stationary processes. However, the extraction of diagnostic information from the real world vibration signals is a delicate task requiring the application of sophisticated signal processing techniques, tailored for specific machines operating under restricted conditions. Such difficulty in the current state of the art of vibration analysis forces the industry to apply methods with reduced diagnostic capabilities but higher adaptability. However in doing so most of the potential of vibration analysis is lost and advanced techniques become of use only for academic endeavours. The aim of this document is to reduce the gap between industrial and academic applications of condition monitoring, offering ductile and automated tools which still show high detection capabilities. Three main lines of research are presented in this document. Firstly, the implementation of stochastic resonance in an electrical circuit to enhance directly the analog signal from an accelerometer, in order to lower the computational requirements in the next digital signal processing step. Secondly, the extension of already well-established digital signal processing techniques, cepstral prewhitening and spectral kurtosis, to a wider range of operating conditions, proving their effectiveness in the case of non-stationary speeds. Thirdly, the main contribution of the thesis: the introduction of two novel techniques capable of separating the vibrations of a defective component from the overall vibrations of the machine, by means of a threshold in the amplitude spectrum. After the separation, the cyclic content of the vibration signal is extracted and the thresholded signals provide an enhanced detection. The two proposed methods, phase editing and amplitude cyclic frequency decomposition, are both intuitive and of low computational complexity, but show the same capabilities as more sophisticated state of the art techniques. Furthermore, all these tools have been successfully tested on numerically simulated signals as well as on real vibration data from different machinery, lasting from laboratory test rigs to wind turbines drive-trains and aircraft engines. So in conclusion, the proposed techniques are a promising step toward the full exploitation of condition based maintenance in industrial contexts.

This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office(Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, developand validate a software solution for the detection and diagnosis of a specific type of elevator andrudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flightcontrol computers on board in-series aircraft. LCO is a generic mathematical term defining aninitial condition-independent periodic mode occurring in nonconservative nonlinear systems. Thisstudy focuses on the LCO phenomenon induced by mechanical freeplays in the control surface ofa civil aircraft. The LCO consequences are local structural load augmentation, flight handlingqualities deterioration, actuator operational life reduction, cockpit and cabin comfort deteriorationand maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection anddiagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the controlsurfaces. This study is thought to propose a data-driven solution to help LCO and freeplaydiagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costsby providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason,two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. Areal time detector for LCO diagnosis is first proposed based on the theory of the generalized likeli hood ratio test (GLRT). Some variants and simplifications are also proposed to be compliantwith the industrial constraints. In a second part of this work, a mechanical freeplay detector isintroduced based on the theory of Wiener model identification. Parametric (maximum likelihoodestimator) and non parametric (kernel regression) approaches are investigated, as well as somevariants to well-known nonparametric methods. In particular, the problem of hysteresis cycleestimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrainedand unconstrained problems are studied. A theoretical, numerical (simulator) and experimental(flight data and laboratory) analysis is carried out to investigate the performance of the proposeddetectors and to identify limitations and industrial feasibility. The obtained numerical andexperimental results confirm that the proposed GLR test (and its variants/simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computationalcost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relativelylarge freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

<p>In order to stay competitive, it is necessary for companies to continuously increase the effectiveness and efficiency of their production processes. High availability has, thus, increased in importance. Therefore, maintenance has gained in importance as a support function for ensuring, e.g., quality products and on-time deliveries. Maintenance, though, is a costly support function. It has been reported that as much as 70% of the total production cost can be spent on maintenance. Further, as much as one-third of the cost of maintenance is incurred unnecessarily due to bad planning, overtime cost, limited or misused preventive maintenance, and so on. In so, condition based maintenance is introduced as one solution for a more effective maintenance.</p><p>In condition based maintenance, critical item characteristics are monitored in order to gain early indications of an incipient failure. Research, though, has shown that condition based maintenance has not been implemented on a wide basis. Therefore, the purpose of this research is to investigate how a condition based maintenance approach can be implemented in an industrial setting, and to develop a method that can assist companies in their implementation efforts. Further, the research has been divided in three research questions. They focus on: constituents of a condition based maintenance approach, decision-making prior implementation of condition based maintenance, and finally, the implementation of condition based maintenance in a company.</p><p>By using a systems approach and a case study process, how condition based maintenance can be implemented as a routine has been investigated. The result is an implementation method in which four suggested phases are presented. The method starts with a feasibility test. It then continues with an analysis phase, an implementation phase, and an assessment phase. The conclusions can be summarized as follows: implementing condition based maintenance consists of many general enabling factors, including management support, education and training, good communication, and motivation etc.</p>

<p>Condition Monitoring is an area that has seen substantial growth in the last few decades. The purpose for implementing condition monitoring in industry is to increase productivity, decrease maintenance costs and increase safety. Therefore, condition monitoring can be used not only for planning maintenance but also for allowing the selection of the most efficient equipment to minimize operating costs. </p><p>Hydraulic systems are widely used in industry, aerospace and agriculture and are becoming more complex in construction and in function. Reliability of the systems must be supported by an efficient maintenance scheme. Due to component wear or failure, some system parameters may change causing abnormal behaviour in each component or in the overall circuit. Research in this area has been substantial, and includes specialized studies on artificial fault simulation at the University of Saskatchewan. In this research, an axial pump was the focus of the study. In an axial piston pump, wear between the various faces of components can occur in many parts of the unit. As a consequence, leakage can occur in locations such as between the valve plate and barrel, the drive shaft and oil wiper, the control piston and piston guide, and the swash plate and slippers. In this study, wear (and hence leakage) between the pistons and cylinder bores in the barrel was of interest. Researchers at the University of Saskatchewan, as well as at other research institutions, have been involved in studies to detect wear in pumps using a variety of condition monitoring algorithms. However, to verify the reliability and indeed, limitations of some of the approaches, it is necessary to test the algorithms on systems with real leakage. To introduce actual wear in the piston of pumps can be very difficult and very expensive. Hence, introducing piston wear in an artificial manner would be of great benefit in the evaluation of various condition monitoring techniques.</p><p>Since leakage is a direct consequence of piston wear, it is logical to conclude that varying the leakage in some prescribed manner can be used to artificially simulate wear. A prime concern, therefore, is to be able to precisely understand the dynamic relationships between the wear and leakage and the effect it has on the output flow or pressure waveform from the pump.</p><p>Introducing an artificial leakage to simulate the wear of pistons is a complex task. The creation of an artificial leakage path was not simply a process of providing a resistive short to the tank at the outlet of the pump port as was done in other studies. The objective was to create a leakage environment that would simulate leakage from a single piston (or combination of several pistons thereof). The complexity of the flow and pressure ripple waveforms (which various condition monitoring algorithms did require) was such that a more comprehensive leakage behaviour had to be modeled and experimentally created. A pressure control servo valve with a very high frequency response was employed to divert the flow from the pump outlet with a prescribed waveform directly to the tank to simulate the piston leakage from the high pressure discharge chamber to the pump case drain chamber as the simulated worn piston made contact with the high pressure chamber. The control algorithm could mimic the action of a single worn piston at various degrees of wear. The experimental results indicated that the experimental system could successfully introduce artificial leakage into the pump which was quite consistent with a unit with a real worn piston. Comparisons of the pressure ripples from an actual faulty pump (worn piston) and the artificial faulty pump (artificial leakage) are presented.</p>

Slow speed rotating machines are the mainstay of several industrial applications worldwide. They can be found in paper and steel mills, rotating biological contractors, wind turbines etc. Operational experience of such machinery has not only revealed the early design problems but has also presented opportunities for further significant improvements in the technology and economics of the machines. Slow speed rotating machinery maintenance, mostly related to bearings, shafts and gearbox problems, represents the cause of extended outages. Rotating machinery components such as gearboxes, shafts and bearings degrade slowly with operating time. Such a slow degradation process can be identified if a robust on-line monitoring and predictive maintenance technology is used to detect impending problems and allow repairs to be scheduled. To keep machines functioning at optimal levels, failure detection of such vital components is important as any mechanical degradation or wear, if is not impeded in time, will often progress to more serious damage affecting the operational performance of the machine. This requires far more costly repairs than simply replacing a part. Over the last few years there have been many developments in the use of Acoustic Emission (AE) technology and its analysis for monitoring the condition of rotating machinery whilst in operation, particularly on slow speed rotating machinery. Unlike conventional technologies such as thermography, oil analysis, strain measurements and vibration, AE has been introduced due to its increased sensitivity in detecting the earliest stages of loss of mechanical integrity. This programme of research involves laboratory tests for monitoring slow speed rotating machinery components (shafts and bearings) using AE technology. To implement this objective, two test rigs have been designed to assess the capability of AE as an effective tool for detection of incipient defects within low speed machine components (e.g. shafts and bearings). The focus of the experimental work will be on the initiation and growth of natural defects. Further, this research work investigates the source characterizations of AE signals associated with such bearings whilst in operation. It is also hoped that at the end of this research program, a reliable on-line monitoring scheme used for slow speed rotating machinery components can be developed.

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

<p>Condition Monitoring is an area that has seen substantial growth in the last few decades. The purpose for implementing condition monitoring in industry is to increase productivity, decrease maintenance costs and increase safety. Therefore, condition monitoring can be used not only for planning maintenance but also for allowing the selection of the most efficient equipment to minimize operating costs. </p><p>Hydraulic systems are widely used in industry, aerospace and agriculture and are becoming more complex in construction and in function. Reliability of the systems must be supported by an efficient maintenance scheme. Due to component wear or failure, some system parameters may change causing abnormal behaviour in each component or in the overall circuit. Research in this area has been substantial, and includes specialized studies on artificial fault simulation at the University of Saskatchewan. In this research, an axial pump was the focus of the study. In an axial piston pump, wear between the various faces of components can occur in many parts of the unit. As a consequence, leakage can occur in locations such as between the valve plate and barrel, the drive shaft and oil wiper, the control piston and piston guide, and the swash plate and slippers. In this study, wear (and hence leakage) between the pistons and cylinder bores in the barrel was of interest. Researchers at the University of Saskatchewan, as well as at other research institutions, have been involved in studies to detect wear in pumps using a variety of condition monitoring algorithms. However, to verify the reliability and indeed, limitations of some of the approaches, it is necessary to test the algorithms on systems with real leakage. To introduce actual wear in the piston of pumps can be very difficult and very expensive. Hence, introducing piston wear in an artificial manner would be of great benefit in the evaluation of various condition monitoring techniques.</p><p>Since leakage is a direct consequence of piston wear, it is logical to conclude that varying the leakage in some prescribed manner can be used to artificially simulate wear. A prime concern, therefore, is to be able to precisely understand the dynamic relationships between the wear and leakage and the effect it has on the output flow or pressure waveform from the pump.</p><p>Introducing an artificial leakage to simulate the wear of pistons is a complex task. The creation of an artificial leakage path was not simply a process of providing a resistive short to the tank at the outlet of the pump port as was done in other studies. The objective was to create a leakage environment that would simulate leakage from a single piston (or combination of several pistons thereof). The complexity of the flow and pressure ripple waveforms (which various condition monitoring algorithms did require) was such that a more comprehensive leakage behaviour had to be modeled and experimentally created. A pressure control servo valve with a very high frequency response was employed to divert the flow from the pump outlet with a prescribed waveform directly to the tank to simulate the piston leakage from the high pressure discharge chamber to the pump case drain chamber as the simulated worn piston made contact with the high pressure chamber. The control algorithm could mimic the action of a single worn piston at various degrees of wear. The experimental results indicated that the experimental system could successfully introduce artificial leakage into the pump which was quite consistent with a unit with a real worn piston. Comparisons of the pressure ripples from an actual faulty pump (worn piston) and the artificial faulty pump (artificial leakage) are presented.</p>

Machine tool components are widely used in many industrial applications. In accordance with their usage, a reliable health monitoring system is necessary to detect defects in these components in order monitor machinery performance and avoid malfunction. Even though several techniques have been reported for fault detection and diagnosis, it is a challenging task to implement a condition monitoring system in real world applications due to their complexity in structure and noisy operating environment. The primary objective of this thesis is to develop novel intelligent algorithms for a reliable fault diagnosis of machine tool components. Another objective is to use Micro Electro Mechanical System (MEMS) sensor and interface it with Raspberry pi hardware for the real time condition monitoring. Primarily knowledge based approach with morphological operators and Fuzzy Inference System is proposed, the e˙ectiveness of this approach lies in the selection of structuring elements(SEs). When this is evaluated with di˙erent classes of bearing fault signals, it is able to detect the fault frequencies e˙ectively. Secondarily, An analytical approach with multi class support machine is proposed, this method has uniqueness of learning on its own with out any prior knowledge, the e˙ectiveness of this method lies on selected features and used kernel for converging. Results have shown that RBF (Radial Bias Function) kernel, which is commonly known as gauss kernel has good performance in identifying faults with less computation time. An idea of prototyping these methods has triggered in using Micro Electro Mechanical System (MEMS) sensor for data acquisition and real time Condition Monitoring. LIS3DH accelerometer sensor is used for the data acquisition of spindle for capturing high frequency fault signals. The measured data is analyzed and compared with the industrial sensor k-shear accelerometer type 8792A.

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In order to stay competitive, it is necessary for companies to continuously increase the effectiveness and efficiency of their production processes. High availability has, thus, increased in importance. Therefore, maintenance has gained in importance as a support function for ensuring, e.g., quality products and on-time deliveries. Maintenance, though, is a costly support function. It has been reported that as much as 70% of the total production cost can be spent on maintenance. Further, as much as one-third of the cost of maintenance is incurred unnecessarily due to bad planning, overtime cost, limited or misused preventive maintenance, and so on. In so, condition based maintenance is introduced as one solution for a more effective maintenance. In condition based maintenance, critical item characteristics are monitored in order to gain early indications of an incipient failure. Research, though, has shown that condition based maintenance has not been implemented on a wide basis. Therefore, the purpose of this research is to investigate how a condition based maintenance approach can be implemented in an industrial setting, and to develop a method that can assist companies in their implementation efforts. Further, the research has been divided in three research questions. They focus on: constituents of a condition based maintenance approach, decision-making prior implementation of condition based maintenance, and finally, the implementation of condition based maintenance in a company. By using a systems approach and a case study process, how condition based maintenance can be implemented as a routine has been investigated. The result is an implementation method in which four suggested phases are presented. The method starts with a feasibility test. It then continues with an analysis phase, an implementation phase, and an assessment phase. The conclusions can be summarized as follows: implementing condition based maintenance consists of many general enabling factors, including management support, education and training, good communication, and motivation etc.