Browsing by Author "Ochella, Sunday"

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    Adopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extension
    (Elsevier, 2021-03-13) Ochella, Sunday; Shafiee, Mahmood; Sansom, Christopher L.
    Many machine learning algorithms and models have been proposed in the literature for predicting the remaining useful life (RUL) of systems and components that are subject to condition monitoring (CM). However, in cases where data is ubiquitous, identifying the most suitable equipment for life-extension based on CM data and RUL predictions is a rather challenging task. This paper proposes a technique for determining and prioritizing high-value assets for life-extension treatments when they reach the end of their useful life. The technique exploits the use of key concepts in machine learning (such as data mining and k-means clustering) in combination with an important tool from reliability-centered maintenance (RCM) called the potential-failure (P-F) curve. The RCM process identifies essential equipment within a plant which are worth monitoring, and then derives the P-F curves for equipment using CM and operational data. Afterwards, a new index called the potential failure interval factor (PFIF) is calculated for each equipment or unit, serving as a health indicator. Subsequently, the units are grouped in two ways: (i) a regression model in combination with suitably defined PFIF window boundaries, (ii) a k-means clustering algorithm based on equipment with similar data features. The most suitable equipment for life-extension are identified in groups in order to aid in planning, decision-making and deployment of maintenance resources. Finally, the technique is empirically tested on NASA’s Commercial Modular Aero-Propulsion System Simulation datasets and the results are discussed in detail.
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    Artificial intelligence in prognostics and health management of engineering systems
    (Elsevier, 2021-12-08) Ochella, Sunday; Shafiee, Mahmood; Dinmohammadi, Fateme
    Prognostics and health management (PHM) has become a crucial aspect of the management of engineering systems and structures, where sensor hardware and decision support tools are deployed to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL). Methodologies for PHM are either model-driven, data-driven or a fusion of both approaches. Data-driven approaches make extensive use of large-scale datasets collected from physical assets to identify underlying failure mechanisms and root causes. In recent years, many data-driven PHM models have been developed to evaluate system’s health conditions using artificial intelligence (AI) and machine learning (ML) algorithms applied to condition monitoring data. The field of AI is fast gaining acceptance in various areas of applications such as robotics, autonomous vehicles and smart devices. With advancements in the use of AI technologies in Industry 4.0, where systems consist of multiple interconnected components in a cyber–physical space, there is increasing pressure on industries to move towards more predictive and proactive maintenance practices. In this paper, a thorough state-of-the-art review of the AI techniques adopted for PHM of engineering systems is conducted. Furthermore, given that the future of inspection and maintenance will be predominantly AI-driven, the paper discusses the soft issues relating to manpower, cyber-security, standards and regulations under such a regime. The review concludes that the current systems and methodologies for maintenance will inevitably become incompatible with future designs and systems; as such, continued research into AI-driven prognostics systems is expedient as it offers the best promise of bridging the potential gap.
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    Performance metrics for artificial intelligence (AI) algorithms adopted in prognostics and health management (PHM) of mechanical systems
    (IOP Publishing: Conference Series, 2021-03-04) Ochella, Sunday; Shafiee, Mahmood
    Research into the use of artificial intelligence (AI) algorithms within the field of prognostics and health management (PHM), in particular for predicting the remaining useful life (RUL) of mechanical systems that are subject to condition monitoring, has gained widespread attention in recent years. It is important to establish confidence levels for RUL predictions, so as to aid operators as well as regulators in making informed decisions regarding maintenance and asset life-cycle planning. Over the past decade, many researchers have devised indicators or metrics for determining the performance of AI algorithms in RUL prediction. While most of the popularly used metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), etc. were adapted from other applications, some bespoke metrics are designed and intended specifically for use in PHM research. This study provides a synopsis of key performance indicators (KPIs) that are applied to AI-driven PHM technologies of mechanical systems. It presents details of the application scenarios, suitability of using a particular metric in different scenarios, the pros and cons of each metric, the trade-offs that may need to be made in choosing one metric over another, and some other factors that engineers should take into account when applying the metrics
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    Requirements for standards and regulations in AI-enabled prognostics and health management
    (IEEE, 2021-11-15) Ochella, Sunday; Shafiee, Mahmood; Sansom, Christopher L.
    The fundamental understanding of the core aspects of prognostics and health management (PHM) as a field of practice is somewhat fully established. However, the various approaches used in the field have continuously evolved. With the recent surge in the adoption of artificial intelligence (AI) algorithms for predictive analytics, data-driven PHM is now more prominent. Notwithstanding the popularity of AI approaches, actual adoption and implementation in fielded systems has been minimal. One of the reasons for this is the lag in an ancillary area, which is the development of corresponding standards and regulations to guide the practice. This paper aims to synthesize various studies in the literature regarding standards and regulations in data-driven PHM and then sets out the necessary requirements for a standards and regulations regime to support the full adoption of AI-enabled PHM. An acceptability criterion is proposed, which incorporates the various factors that must be considered for verification, validation, and certification of AI-enabled PHM technologies. The use of the acceptability criterion is demonstrated, which will potentially be very useful to certification bodies and regulatory agencies in the process of approving AI-enabled PHM for use in safety-critical assets.
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    An RUL-informed approach for life extension of high-value assets
    (Elsevier, 2022-06-24) Ochella, Sunday; Shafiee, Mahmood; Sansom, Christopher L.
    The conventional approaches for life-extension (LE) of industrial assets are largely qualitative and focus only on a few indicators at the end of an asset’s design life. However, an asset may consist of numerous individual components with different useful lives and therefore applying a single LE strategy to every component will not result in an efficient outcome. In recent years, many advanced analytics techniques have been proposed to estimate the remaining useful life (RUL) of the assets equipped with sensor technology. This paper proposes a data-driven model for LE decision-making based on RUL values predicted on a real-time basis during the asset’s operational life. Our proposed LE model is conceptually targeted at the component, unit, or subsystem level; however, an asset-level decision is made by aggregating information across all components. Consequently, LE is viewed and assessed as a series of ongoing activities, albeit carefully orchestrated in a manner similar to operation and maintenance (O&M). The application of the model is demonstrated using the publicly available NASA C-MAPSS dataset for large commercial turbofan engines. This approach will be very beneficial to asset owners and maintenance engineers as it seamlessly weaves LE strategies into O&M activities, thus optimizing resources.