Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for continuous operations in machines which can also improve productivity and enhance system safety. In condition-based maintenance (CBM), effective diagnostics and prognostics are important aspects of CBM which provide sufficient time for maintenance engineers to schedule a repair and acquire replacement components before the components finally fail. All machine components have certain characteristics of failure patterns and are subjected to degradation processes in real environments. This paper describes a technique for accurate assessment of the remnant life of machines based on prior expert knowledge embedded in closed loop prognostics systems. The technique uses Support Vector Machines (SVM) for classification of faults and evaluation of health for six stages of bearing degradation. To validate the feasibility of the proposed model, several fault historical data from High Pressure Liquefied Natural Gas (LNG) pumps were analysed to obtain their failure patterns. The results obtained were very encouraging and the prediction closely matched the real life particularly at the end of term of the bearings

MEMS Accelerometers: Testing and Practical Approach for Smart Sensing and Machinery Diagnostics

In the recent years a major change in the engineering process of mechatronics and robotics has taken place. In various design oriented laboratories around the world a shift can be recognised from a focus on analysis, simulation and modelling combined with outsourcing hardware design to the use of digital fabrication tools (laser cutter, 3D printer) allowing a cyclic (iterative) design process inside in the lab. This chapter aims to give an overview of the impact of this change, using many examples from various projects, and will share some insights and lessons learned for facilitating and implementing this process

Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators

The aircraft engines manufacturing industry is subjected to many dependability constraints from certification authorities and economic background. In particular, the costs induced by unscheduled maintenance and delays and cancellations impose to ensure a minimum level of availability. For this purpose, Prognostics and Health Management (PHM) is used as a means to perform online periodic assessment of the engines’ health status. The whole PHM methodology is based on the processing of some variables reflecting the system’s health status named Health Indicators. The collecting of HI is an on-board embedded task which has to be specified before the entry into service for matters of retrofit costs. However, the current development methodology of PHM systems is considered as a marginal task in the industry and it is observed that most of the time, the set of HI is defined too late and only in a qualitative way. In this paper, the authors propose a novel development methodology for PHM systems centered on an anticipated model-based validation of HI. This validation is based on the use of uncertainties propagation to simulate the distributions of HI including the randomness of parameters. The paper defines also some performance metrics and criteria for the validation of the HI set. Eventually, the methodology is applied to the development of a PHM solution for an aircraft engine actuation loop. It reveals a lack of performance of the original set of HI and allows defining new ones in order to meet the specifications before the entry into service

An OSA-CBM Multi-Agent Vehicle Health Management Architecture for Self-Health Awareness

Integrated Vehicle Health Management (IVHM) systems on modern aircraft or autonomous unmanned vehicles should provide diagnostic and prognostic capabilities with lower support costs and amount of data traffic. When mission objectives cannot be reached for the control system since unanticipated operating conditions exists, namely a failure, the mission plan must be revised or altered according to the health monitoring system assessment. Representation of the system health knowledge must facilitate interaction with the control system to compensate for subsystem degradation. Several generic architectures have been described for the implementation of health monitoring systems and their integration with the control system. In particular, the Open System Architecture - Condition-Based Maintenance (OSA-CBM) approach is considered in this work as initial point, and it is evolved in the sense of self-health awareness, by defining an appropriated multi-agent smart health management architecture based on smart device models, communication agents and a distributed control system. A case study about its application on fuel-cells as auxiliary power generator will demonstrate the integration.Postprint (published version

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

Optimal Maintenance Scheduling for Multi-Component E-Manufacturing System

During the recent years, development of information technology caused to develop a new industrial system which is called e-Manufacturing system. Thanks to the webenabled manufacturing technologies, the lead times are being minimized to their extreme level, and the minimum amount of inventory is kept, though the products are being made-to order. Under these circumstances, achieving near-zero downtime of the plant floor’s equipments is a crucial factor which mitigates the risk of facing unmet demands. Many researches carried out to schedule maintenance actions in short term, but none of them have utilized all of planning horizon to spread maintenance actions along available time. In this research a method of enhanced maintenance scheduling of multi-component e-Manufacturing systems has been developed. In this multi-component system, importance of all machines is considered and the benefit of the entire system in term of produced parts is taken into account (versus benefits of single machine). In proposed system, the predicted machines degradation information, online information about work in process (WIP) inventory (at inventory buffer of each work station) as well as production line’s dynamism are taken into account. All of makespans of planning horizon have been utilized to improve scheduling efficiency and operational productivity by maximizing the system throughputs. A state-of-the-art method which is called simulation optimization has been utilized to implement the proposed scheduling method. The production system is simulated by ProModel software. It plays the role of objective function of the maintenance scheduling optimization problem. Using a production related heuristic method which is called system value method, the value of each workstation is determined. These values are used to define the objective function’s parameters. Then, using genetic algorithm-based software which is called SimRunner and has been embedded by ProModel, the scheduling optimization procedure is run to find optimum maintenance schedule. This process is carried out for nine generated scenarios. At the end, the results are benchmarked by two commonly used maintenance scheduling methods to magnify the importance of proposed intelligent maintenance scheduling in the multi-component e-Manufacturing systems. The results demonstrate that the proposed optimal maintenance scheduling method yields much better system value rather than sequencing methods. Furthermore, it indicates that when the mean time to repairs are longer, this method is more efficient. The results in the simulated testbed indicate that the developed scheduling method using simulation optimization functions properly and can be applied in other cases

A Predictive maintenance model for heterogeneous industrial refrigeration systems

The automatic assessment of the degradation state of industrial refrigeration systems is becoming increasingly important and constitutes a key-role within predictive maintenance approaches. Lately, data-driven methods especially became the focus of research in this respect. As they only rely on historical data in the development phase, they offer great advantages in terms of flexibility and generalisability by circumventing the need for specific domain knowledge. While most scientific contributions employ methods emerging from the field of machine learning (ML), only very few consider their applicability amongst different heterogeneous systems. In fact, the majority of existing contributions in this field solely apply supervised ML models, which assume the availability of labelled fault data for each system respectively. However, this places restrictions on the overall applicability, as data labelling is mostly conducted by humans and therefore constitutes a non-negligible cost and time factor. Moreover, such methods assume that all considered fault types occurred in the past, a condition that may not always be guaranteed to be satisfied. Therefore, this dissertation proposes a predictive maintenance model for industrial refrigeration systems by especially addressing its transferability onto different but related heterogeneous systems. In particular, it aims at solving a sub-problem known as condition-based maintenance (CBM) to automatically assess the system’s state of degradation. To this end, the model does not only estimate how far a possible malfunction has progressed, but also determines the fault type being present. As will be described in greater detail throughout this dissertation, the proposed model also utilises techniques from the field of ML but rather bypasses the strict assumptions accompanying supervised ML. Accordingly, it assumes the data of the target system to be primarily unlabelled while a few labelled samples are expected to be retrievable from the fault-free operational state, which can be obtained at low cost. Yet, to enable the model’s intended functionality, it additionally employs data from only one fully labelled source dataset and, thus, allows the benefits of data-driven approaches towards predictive maintenance to be further exploited. After the introduction, the dissertation at hand introduces the related concepts as well as the terms and definitions and delimits this work from other fields of research. Furthermore, the scope of application is further introduced and the latest scientific work is presented. This is then followed by the explanation of the open research gap, from which the research questions are derived. The third chapter deals with the main principles of the model, including the mathematical notations and the individual concepts. It furthermore delivers an overview about the variety of problems arising in this context and presents the associated solutions from a theoretical point of view. Subsequently, the data acquisition phase is described, addressing both the data collection procedure and the outcome of the test cases. In addition, the considered fault characteristics are presented and compared with the ones obtained from the related publicly available dataset. In essence, both datasets form the basis for the model validation, as discussed in the following chapter. This chapter then further comprises the results obtained from the model, which are compared with the ones retrieved from several baseline models derived from the literature. This work then closes with a summary and the conclusions drawn from the model results. Lastly, an outlook of the presented dissertation is provide

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change