This thesis presents a new method for Condition Based Ultrasonic Monitoring to be applied in conjunction with a lubrication distribution controller. As part of this thesis, algorithms were developed using ultrasonic sensors to control the application of lubrication to machinery. The controller sensors detect an ultrasonic signal from rolling or sliding machine elements. This signal then alerts the controller to dispense the proper amount of lubrication when needed, as opposed to a time schedule based on average performance or history. The work from this thesis will be used to help reduce equipment downtime and maintenance cost when utilized in an industrial environment.

Indiana University-Purdue University Indianapolis (IUPUI) / Health IT has recently seen a significant progress with the nationwide migration of several hospitals from legacy patient records to standardized Electronic Health Record (EHR) and the establishment of various Health Information Exchanges that facilitate access to patient health data across multiple networks. While this progress is a major enabler of improved health care services, it is unable to deliver the continuum of the patient's current and historical health data needed by emerging trends in medicine. Fields such as precision and preventive medicine require longitudinal health data in addition to complementary data such as social, demographic and family history. This thesis introduces a person health record (PHR) which overcomes the above gap through a personalized framework that organizes health data according to the patient’s disease condition. The proposed personalized person health record (P2HR) represents a departure from the standardized one-size-fits-all model of currently available PHRs. It also relies on a hybrid peer-to-peer model to facilitate patient provider communication. One of the core challenges of the proposed framework is the mapping between the event-based data model used by current EHRs and PHRs and the proposed condition-based data model. Effectively mapping symptoms and measurements to disease conditions is challenging given that each symptom or measurement may be associated with multiple disease conditions. To alleviate these problems the proposed framework allows users and their health care providers to establish the relationships between events and disease conditions on a case-by-case basis. This organization provides both the patient and the provider with a better view of each disease condition and its progression.

As Industry 4.0 and Internet of Things are established across factories and enterprises, the interest for learning more about these concepts and the possibilities they provide for condition based maintenance is expressed by a factory in Sweden. By addressing the aspects of Internet of Things and Industry 4.0, a system for performing data acquisition from sensors in an industrial environment is developed using Arrowhead Framework. This framework is evaluated around its suitability for this kind of application, and regarding what the framework may provide to the factory compared to other solutions and systems. A solution featuring a system based on Arrowhead Framework is developed, implemented, and briefly tested. The system is successful in performing data acquisition, and Arrowhead Framework is considered a viable option that may be used to provide a system tailored for different purposes, presumed that the factory is prepared to allocate resources on developing a solution around it.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Modern aircraft and space vehicles routinely sense and record vast quantities of information relevant to assessing the vehicles’ health. However, limitations imposed by the bandwidth of telemetry and network connections prevent real-time transmission of the complete data set to central stations for analysis. An online health-management system suitable for bandwidth-limited network environments that enables interrogation of the full data set by ground-based operators is described. The system uses distributed objects organized in a data processing hierarchy linked by a buffered data-management subsystem. Reduced health information is routinely transmitted, but dynamic reports may be requested on demand from any object.

Wind is becoming an increasingly important source of energy for countries that ratify to reduce the emission of greenhouse gases and mitigate the effects of global warming. Investments in wind farms are affected by inter-related assets and stakeholders’ requirements. These requirements demand a well-founded Asset Management (AM) frame-work which is currently lacking in the wind industry. Drawing from processes, tools and techniques of AM in other industries, a structured model for AM in the wind industry is developed. The model divulges that maintenance is indispensable to the core business objectives of the wind industry. However, the common maintenance strategies applied to wind turbines are inadequate to support the current commercial drivers of the wind industry. Consequently, a hybrid approach to the selection of a suitable maintenance strategy is developed. The approach is used in a case study to demonstrate its practical application. Suitable Condition-Based Maintenance activities for wind turbines are determined. Maintenance optimisation is a means to determine the most cost-effective maintenance strategy. Field failure and maintenance data of wind turbines are collected and analysed using two quantitative maintenance optimisation techniques; Modelling System Failures (MSF) and Delay-Time Maintenance Model (DTMM). The MSF permits the evaluation of life-data samples and enables the design and simulation of a system’s model to determine optimum maintenance activities. Maximum Likelihood Estimation is used to estimate the shape (β) and scale (η) parameters of the Weibull distribution for critical components and subsystems of the wind turbines. Reliability Block Diagrams are designed using the estimated β and η to model the failures of the wind turbines and of a selected wind farm. The models are simulated to assess and optimise the reliability, availability and maintainability of the wind turbine and the farm. The DTMM examines equipment failure patterns by taking into account failure consequences, inspection time and cost in order to determine optimum inspection intervals. Defects rate (α) and mean delay-time (1/γ) of components and subsystems within the wind turbine are estimated. Optimal inspection intervals for critical subsystems of the wind turbine are then determined.

One of the overarching goals for military aircraft sustainment is to keep a high proportion of aircraft available despite the need for maintenance. Traditional solutions to this problem require conservative resource estimates, but this is costly. In recent years an overall paradigm shift towards affordability has created pressure to find other options for achieving high values of fleet level metrics. Past efforts at increasing affordability have had mixed success, and as a result such strategies need to be tested early on in the lifetime of a product, ideally before the product is ever fielded. In order to provide the ability to evaluate the effects of sustainment decisions such as different maintenance paradigms and cost goals, this thesis develops a sustainment modeling environment, known as Sustain-ME, to facilitate open analysis based on the best information available. The goal of creating Sustain-ME is to allow decision makers to define a sustainment scenario and compare different decisions of interest on a common basis. Sustain-ME is a discrete event simulation, which means it efficiently provides a reasonable prediction of operational behavior. This thesis describes the information used to construct Sutain-ME, including the assumptions made for many of the parameters of the modeled sustainment process. It next verifies the behavior of the different elements that make up the sustainment model including operations, maintenance, maintenance paradigms, and the supply chain. Finally a methodology for using SustainME is defined and a demonstration of the types of studies Sustain-ME was built to perform is shown. The demonstration compares three different maintenance paradigms: reactive maintenance, condition based maintenance, and a novel CBM paradigm known as CBM-MiMOSA.

This research develops an equipment failure prognostics model to predict the equipment’s chance of survival, using LAD. LAD benefits from not relying on any statistical theory, which enables it to overcome the problems concerning the statistical properties of the datasets. Its main advantage is its straightforward process and self-explanatory results. Herein, our main objective is to develop models to calculate equipment’s survival probability at a certain future moment, using LAD. We employ the LAD’s pattern generation procedure. Then, we introduce a guideline to employ generated patterns to estimate the equipment’s survival probability. The models are applied on a condition monitoring dataset. Performance analysis reveals that they provide comprehensible results that are greatly beneficial to maintenance practitioners. Results are compared with PHM’s results. The comparison reveals that the LAD models compare favorably to the PHM. Since they are at their beginning phase, some future directions are presented to improve their performances.

Condition-based maintenance is an important aspect in various industries to ensure reliable operation of machinery. To successfully execute maintenance responsibilities, it is required to know which components are healthy and which are in a damaged state. Thus, the need for effective incipient fault detection requires a method that can separate fault signatures from operating condition information. Conventional gearbox monitoring techniques assume that a change in the vibration signal is caused by the presence of a fault. Under constant operating conditions this assumption may be valid, but under fluctuating conditions the assumption does not hold. Fluctuating operating conditions are inevitable for gearboxes in mining and wind turbine industries due to fluctuating ground and wind properties. The fluctuating conditions cause smearing of the signal frequency spectrum and valuable diagnostic information is lost when using classical condition monitoring techniques. More sophisticated signal processing techniques are therefore needed to effectively diagnose incipient faults to make informed asset management decisions. In this dissertation, envelope analysis, which has long been recognized as one of the best methods for bearing fault diagnosis, is used as the primary diagnostic tool. A common precursor to envelope analysis is bandpass filtering which is aimed at emphasising bearing faults and removing background noise and deterministic components. Identification and optimal selection of the informative frequency band which contains damage related information is the focus area for research in this dissertation. Many automatic band selection techniques exist and have proven effective under constant speed conditions. However, it has been shown that these techniques occasionally identify frequency bands that contain non-damage related information, especially under fluctuating speeds and low damage levels. With this research, a new methodology is proposed which makes use of popular informative frequency band selection techniques, such as the Fast Kurtogram amongst others, to effectively identify damage under constant and fluctuating speed conditions. The proposed methodology uses both healthy and damaged vibration signals to identify novelty information. In doing so, the method can also identify damage earlier than existing methods. The technique is designed to ignore potentially dominant deterministic components which would lead to incorrect band selection for envelope analysis. Furthermore, pre-whitening of vibration signals is a common technique to enhance the bearing signal-to-noise ratio. Without pre-whitening, random noise and deterministic components often dominate the bearing fault signatures and render existing diagnostic techniques ineffective. The proposed methodology is shown to be more robust than existing automatic band selection methods because it requires no pre-whitening. By using both healthy and damaged signals, the proposed methodology favours frequency bands that contain damage related information. The findings in this dissertation are validated on a range of synthetic signals as well as on actual experimental data. The synthetic signals are constructed from a phenomenological gearbox model where the exact operating and bearing condition can be controlled. The experimental results are statistically compared for a wide range of signals and damage levels such that the robustness of the proposed method can be critically evaluated. It was found that the new method is capable of outperforming existing methods in terms of percentage classification of bearing signals with outer race damage and can detect damage with smaller fault severity. / Dissertation (MEng)--University of Pretoria, 2019. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

In this thesis, the COMAC ARJ21 fuselage’s final assembly process is used as a case study. High production rate (i.e. number of aircraft assembled per year) with reasonable cost is the overall aim in this example. The output of final assembly will essentially affect the prior and subsequent processes of the overall ARJ21 production. From the collected field data, it was identified that a number of disruptions (or bottlenecks) in the assembly sequence were caused by breakdowns and maintenance of the (semi-)automatic assembly machines like portable computer numerical control (CNC) drilling machine, rivet gun and overhead crane. The focus of this thesis is therefore on the maintenance strategies (i.e. Condition-Based Maintenance (CBM)) for these equipment and how they impact the throughput of the fuselage assembly process. The fuselage assembly process is modelled and analysed by using agent-based simulation in this thesis. The agent approach allows complex process interactions of assembly, equipment and maintenance to be captured and empirically studied. In this thesis, the built network is modelled as the sequence of activities in each stage. Each stage is broken down into critical activities which are parameterized by activity lead-time and equipment used. CBM based models of uncertain degradation and imperfect maintenance are used in the simulation study. A scatter search is used to find multi-objective optimal solutions for the CBM regime, where the maintenance-related cost and production rate are the optimization objectives. In this thesis, in order to ease computation intensity caused by running multiple simulations during the optimization and to simplify a multi-objective formulation, multiple Min-Max weightings are applied to trace Pareto front. The empirical analysis reviews the trade-offs between the production rate and maintenance cost and how these objectives are influenced by the design parameters.

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.