Optimal Cost-Effective Maintenance Policy for a Helicopter Gearbox Early Fault Detection under Varying Load

Most of the existing fault detection methods rarely consider the cost-optimal maintenance policy. A novel multivariate Bayesian control approach is proposed, which enables the implementation of early fault detection for a helicopter gearbox with cost minimization maintenance policy under varying load. A continuous time hidden semi-Markov model (HSMM) is employed to describe the stochastic relationship between the unobservable states and observable observations of the gear system. Explicit expressions for the remaining useful life prediction are derived using HSMM. Considering the maintenance cost in fault detection, the multivariate Bayesian control scheme based on HSMM is developed; the objective is to minimize the long-run expected average cost per unit time. An effective computational algorithm in the semi-Markov decision process (SMDP) framework is designed to obtain the optimal control limit. A comparison with the multivariate Bayesian control chart based on hidden Markov model (HMM) and the traditional age-based replacement policy is given, which illustrates the effectiveness of the proposed approach

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

A data-driven failure prognostics method based on mixture of gaussians hidden markov models

International audienceThis paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings. The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM). The method relies on two phases: an off-line phase, and an on-line phase. During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients. The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon. The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence. The developed method is tested on benchmark data taken from the "NASA prognostics data repository" related to several experiments of failures on bearings done under different operating conditions. Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper. The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs. Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions. In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure. Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance

Modelling of the Military Helicopter Operation Process in Terms of Readiness

The processes of exploitation of military objects are usually characterised by the specificity of the operation and the complexity of both the process itself and the object. This specificity may relate both to the type of tasks that these objects carry out and to the environment in which these processes take place. Complexity is usually reflected in the very structure of an object (for example, a ship, an aircraft or a helicopter) and, consequently, in its operation/maintenance system. The above mentioned features, as well as the limited access to data, naturally limits the set of publications available on this subject. In this article, the authors have presented a method of assessing the readiness of military helicopters operated by the Armed Forces of the Republic of Poland. The readiness of technical objects used in military exploitation systems is a basic indicator of equipment preparation for executing tasks. In exploitation process research, the mathematical models are usually discrete in states and continuous in time stochastic processes, in the set of which Markov models are included. The paper presents an example of using Markov processes with discrete time and with continuous time to assess the readiness of a technical object performing tasks appearing in random moments of time. At the same time, the aim of the examined system to achieve a state of balance is presented

A Stochastic Approach to Measurement-Driven Damage Detection And Prognosis in Structural Health Monitoring

Damage detection and prognosis are integral to asset management of critical mechanical and civil engineering infrastructure. In practice, these two aspects are often decoupled, where the former is carried out independently using sensor data (e.g., vibrations), while the latter is undertaken based on reliability principles using life time failure data of the system or the component of interest. Only in a few studies damage detection results are extended to remaining useful life estimation, which is achieved by modeling the underlying degradation process using a surrogate measure of degradation. However, an integrated framework which undertakes damage detection, prognosis, and maintenance planning in a systematic way is lacking in the literature. Furthermore, the parameters of degradation model which are utilized for prognosis are often solely estimated using the degradation data obtained from the monitored unit, which represents the degradation of a specific unit, but ignores the general population trend. The main objectives of this thesis are three-fold: first, a mathematical framework using surrogate measure of degradation is developed to undertake the damage detection and prognosis in a single framework; next, the prior knowledge obtained from the historical failed units are integrated in model parameter estimation and residual useful life (RUL) updating of a monitored unit using a Bayesian approach; finally, the proposed degradation modeling framework is applied for maintenance planning of civil and industrial systems, specifically, for reinforced concrete beams and rolling element bearings. The initiation of a fault in these applications is often followed by a sudden change in the degradation path. The location of a change-point can be associated with a sudden loss of stiffness in the case of structural members, or fault initiation in the case of bearings. Hence, in this thesis, the task of change point location identification is thought of as being synonymous with damage or fault detection in the context of structural health monitoring. Furthermore, the change point results are used for two-phase degradation modeling, future degradation level prediction and subsequent RUL estimation. The model parameters are updated using a Bayesian approach, which systematically integrates the prior knowledge obtained from historical failure-time data with monitored data obtained from an in-situ unit. Once such a model is established, it is projected to a failure threshold, thereby allowing for RUL estimation and maintenance planning. Results from the numerical as well as actual field data shows that the proposed degradation modeling framework is good in performing these two tasks. It was also found that as more degradation data is utilized from the monitoring unit, the progressing fault is detected in a timely manner and the model parameters estimates and the end life predictions become more accurate

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Optimal Replacement Strategies for Wind Energy Systems

Motivated by rising energy prices, global climate change, escalating demand for electricity and global energy supply uncertainties, the U.S. government has established an ambitious goal of generating 80% of its electricity supply from clean, renewable sources by 2035. Wind energy is poised to play a prominent role in achieving this goal as it is estimated that 20% of the total domestic electricity supply can be reliably generated by land-based and offshore wind turbines by 2030. However, the cost of producing wind energy remains a significant barrier with operating and maintenance (O&M) costs contributing 20 to 47.5% of the total cost of energy. Given the urgent need for clean, renewable energy sources, and the widespread appeal of wind energy as a viable alternative, it is imperative to develop effective techniques to reduce the O&M costs of wind energy. This dissertation presents a framework within which real-time, condition-based data can be exploited to optimally time the replacement of critical wind turbine components. First, hybrid analytical-statistical tools are developed to estimate the current health of the component and approximate the expected time at which it will fail by observing a surrogate signal of degradation. The signal is assumed to evolve as a switching diffusion process, and its parameters are estimated via a novel Markov chain Monte Carlo procedure. Next, the problem of optimally replacing a critical component that resides in a partially-observable environment is addressed. Two models are formulated using a partially-observed Markov decision process (POMDP) framework. The first model ignores the cost of turbine downtime, while the second includes this cost explicitly. For both models, it is shown that a threshold replacement policy is optimal with respect to the cumulative level of component degradation. A third model is presented that considers cases in which the environment is partially observed and degradation measurements are uncertain. A threshold policy is shown to be optimal for a special case of this model. Several numerical examples will illustrate the main results and the value of including environmental observations in the wind energy setting

Systems Engineering: Availability and Reliability

Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE’2020 conference. This conference and journal’s Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling

Prognostics and health management for multi-component systems

The ever increasing number of manufacturing requirements is pushing original equipment manufacturers (OEM) to design more complex systems to meet industrial needs. These systems are being fitted with more components which bear stochastic and economic dependencies. Therefore maintaining such systems is becoming more and more of a challenge, especially due to their degradation processes becoming highly stochastic in nature. This thesis is concerned with the prognostics and health management (PHM) of such complex multi-component systems, whereby signal processing and health indicator extraction, diagnostics, prognostics and maintenance decision making in light of present stochastic and economic dependencies are considered. We introduce several novel approaches for dealing with systems that have multiple components. We first introduce a gearbox accelerated life testing platform that was designed with the objective of gathering experimental data for multi-component degradation models, for the reason that multi-component systems with inter-dependencies follow a highly stochastic degradation process which depends to an extent on their complex mechanical design. We then present our methodology for extracting accurate health indicators from multi-component systems by means of a time-frequency domain analysis. This sets the stage for degradation modelling, and so we show the development of a generic degradation model in which the degradation process of a component may be dependent on the operating conditions, the component's own state, and the state of the other components. We then show how to fit the models to data using particle filter. This method is then used for the data generated by the gearbox. Afterwards a diagnostic procedure is presented and uses Gaussian mixture models. This is used to uncover accelerated wear processes that take place when old worn out components are coupled with new healthy components. Finally economic dependency is considered where combining multiple maintenance activities has lower cost than performing maintenance on components separately. To select a component or components to be preventively maintained, adaptive preventive maintenance and opportunistic maintenance rules are proposed. A cost model is developed to find the optimal values of decision variables. In our work, we find that stochastic dependencies between components lead to accelerated degradation which causes unexpected faults and failures, and consequent economic losses. Although this work deals with stochastic dependence between components, it involves some engineering knowledge of the systems under study, and this makes application of the models on a large scale challenging to automate. Therefore, we make recommendation for future research that includes the development of end-to-end learning techniques such as deep learning. In doing so we can potentially use the time wave data and automatically extract the most relevant features for doing accurate prognostics, and therefore health management, of such systems. The research work in this thesis was motivated by the problems faced by industrial partners such as the world leading food system manufacturing company Marel in the Netherlands, which were part of the sustainable manufacturing and advanced robotics training network in Europe (SMART-e)