Prognostics And Health Management Applications Research Articles

Structured Data Ontology for AI in Industrial Asset Condition Monitoring

This paper proposes an ontology for prognostics and health management (PHM) applications involving sensor networks monitoring industrial machinery. Deep learning methods show promise for the development of autonomous PHM systems but require vast quantities of structured and representative data to realize their potential. PHM systems involve unique and specialized data characterized by time and context, and thus benefit from tailored data management systems. Furthermore, the use of dissimilar standards and practices with respect to database structure and data organization is a hinderance to interoperability. To address this, this paper presents a robust, structured data ontology and schema that is designed to accommodate a wide breadth of PHM applications. The inclusion of contextual and temporal data increases its value for developing and deploying enhanced ML-driven PHM systems. Challenges around balancing the competing priorities of structure and flexibility are discussed. The proposed schema provides the benefits of a relational schema with some provisions for noSQL-like flexibility in areas where PMH applications demand it. The selection of a database engine for implementation is also discussed, and the proposed ontology is demonstrated using a Postgres database. An instance of the database was loaded with large auto-generated fictitious data via multiple Python scripts. CRUD (create, read, update, delete) operations are demonstrated with several queries that answer common PHM questions.

Vibration Signal Decomposition using Dilated CNN

Vibration sensors have gained increasing popularity as valuable tools for Prognostics and Health Management (PHM) applications, enabling early detection of mechanical failures in industrial machines. Vibration signals comprise two main sources of information: periodic vibrations from components, phase-locked to the rotating speed (e.g., gears), and non-deterministic broadband vibrations associated with bearings, structure, and background noise. In PHM applications, it is important to decompose vibrations into these two sources to optimize the use of different diagnostic methods for each signal component. In practice, the decomposition should be cost-effective by working without supplementary information about system operating conditions and kinematics. Existing methods of vibration source separation commonly rely on an auto-regression (AR) model of vibrations and employ adaptive filtering techniques to estimate its parameters. However, these methods suffer from degraded accuracy in complex geared vibrations containing numerous periodic components and requiring large filter length to promise high frequency resolution in component separation. To address these challenges, we propose a new method that utilizes dilated Convolutional Neural Networks (CNNs) instead of adaptive filtering to improve the accuracy of decomposing complex vibration signals, all without the need for any supplementary information. To evaluate the performance of the new method, we conducted experiments using both simulated signals and real-world vibrations. The simulation results demonstrate improved accuracy in signal decomposition when our method is used instead of adaptive filtering. Additionally, the new method applied to real vibrations, showcases significant enhancement in bearing failure detection through accurate isolation of bearing-related vibrations. This study reveals the potential of our new method in various PHM applications requiring highly accurate diagnostics and prognostics in complex geared vibrations, particularly when supplementary information about operating conditions and system kinematics is unavailable.

OSPtk: Cost-aware Optimal Sensor Placement Toolkit Enabling Design-for-PHM in Critical Industrial Systems

Performance of a Prognostics and Health Management (PHM) system in a fielded application depends on observability from existing monitoring equipment and sensing, which get determined at the design phase. Although various technologies have been proposed in the literature, there is currently a lack of known generic tools specifically designed for performing design stage sensor placement analyses from a PHM perspective. This leads to PHM observability being an afterthought and resulting PHM designs being sub-optimal. This paper describes a new Optimal Sensor Placement (OSP) framework, its implementation as a toolkit and the experience with applying it to a new product design in the context of a Small Modular Reactor (SMR). The formulation adds multiple important features that are critical to PHM applications. Firstly, it establishes a direct link to PHM performance requirements with intent to reduce operational and maintenance costs. Moreover, it acknowledges and accounts for the costs and risks of errors that PHM system will incur, and simultaneously considers operational requirements on sensing for performance, control and/or regulatory requirements. The toolkit described here implements formulations of a large number of requirements scenarios applicable in a generic industrial product development setting.

Artificial intelligence-based data-driven prognostics in industry: A survey

In the age of Industry 5.0, prognostics and health management (PHM) is very important for proactive and scheduled maintenance in industrial processes. The target of prognosis is the health state prediction of the system or machine under consideration, hence its Remaining Useful Life RUL. The life of a tool, a part, or a component of the system must be tracked to increase its productivity, reduce human effort and save lives. Data driven prognostics is highly relying on statistical or artificial intelligence AI methods including machine learning (ML) and deep learning (DL) models. AI is a massive enlarging field with encouraging outcomes in prognostics for modelling of data with complex representations and temporal dependencies. A sample of latest research in prognostics especially in industry applications has been collected during this research. About 76% of the collected research papers used data-driven prognostics in their model including 48% applied DL different architectures for prognostic purposes in industrial systems in the last few years. Therefore, this survey concentrate on presenting AI-based data-driven prognostics in industrial systems especially DL-based architectures. The study also puts spot on the main challenges with opportunities of future work in the DL-based PHM applications in the age of Industry 5.0.

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

Prognostics and health management (PHM) is an engineering approach dealing with the diagnosis, prognosis, and management of the health state of engineering systems. Methods that can analyze system behavior, fault conditions, and degradation are crucial for PHM applications, as they create the basis for determining, predicting, and monitoring the health of engineering systems. Data-driven methods have been proven to be suitable for automated diagnosis or prognosis due to their pattern recognition and anomaly detection abilities. Moreover, they do not require knowledge of the underlying degradation process. However, training data-driven methods usually requires a large amount of data, whose collection, cleansing, organization, and preparation are generally very time-consuming and costly. There are usually little or no run-to-failure data available at market launch, especially for new systems such as new machine generations. Nevertheless, related systems, hereinafter referred to as similar systems, often already exist, differing only in some technical characteristics. In this paper, the similar system problem is defined, and explanations of the difficulties that arise when using data from similar systems are presented. Furthermore, it is discussed why the usage of these data offers great potential for condition diagnosis and prognosis of engineering systems. An overview of data-driven methods that can be used to utilize data from similar systems is provided, and the methods that such systems already consider are highlighted. Two related research areas are identified, namely, fleet learning and transfer learning. In the paper, it is shown that similar system approaches will become an important branch of research in PHM. However, some difficulties must be overcome.

Critical Components' Selection Methodology of a Microgripper for Intracorporeal Surgery

Prognostics and Health Management (PHM) is widely used to study the health state of the system. However, the application of PHM for micro medical devices is a big challenge since they are made up of multiple components which are complex. Prognostics approaches for complex microsystems should be oriented towards identifying critical components and monitoring them individually. This implies the importance of identifying as much as possible the components that can lead to system failures. This work proposes a methodology to select critical components of a micro medical device for PHM implementation. The micro device is a microgripper composed of four components and four steel wires of 0.08 mm in diameter. A risk analysis is performed according to the requirements of ISO standard 14971 for medical devices. This standard provides some techniques that allow to identify some failures modes which affect the behavior and performance of the final product. This work can be seen as a necessary preliminary step before data collection and PHM implementation for such tiny devices. For that purpose, the Failure mode and effect analysis (FMEA) technique is used in order to select critical components. The selection of the critical components is based on a criticality analysis (CA) mainly to prioritize and minimize the effects of critical failure modes that can lead to fault propagation.In addition, some practical guides are presented for PHM development in future works.

Virtual Data Generation and Development of RF Module Fault Analysis Tool for PHM Application

Purpose: This study proposes virtual data generation methods and develops a fault analysis tool for the prognostics and health management (PHM) of a radio frequency (RF) module, which is the main component used in electronic warfare devices.BRMethods: Current signals are assumed to be measured on the RF module and selected for virtual data generation. The three fault modes of the RF module are identified with help of an electronic warfare developer group.BRResults: The following fault modes are identified: 1) Delaying initial booting time, 2) continuously decreasing current, and 3) outlier of current signal. Accordingly, different types of signals are generated virtually, and the fault analysis tool is developed in the MATLAB software environment.BRConclusion: Virtual signal generation for different fault modes and its application in the analysis tool can be implemented for other types of electronic warfare as well. In addition, it can be applied to build prognosis models during the initial product development phase, despite the lack of field data.

Opportunities and data requirements for data-driven prognostics and health management in liquid hydrogen storage systems

During the past decade, Prognostics and Health Management (PHM) has become an important set of tools in various areas of industry and academic reliability engineering. PHM consists of a variety of mathematical and computational methods used to support data-driven decision-making to increase the safety, availability, and reliability of complex engineering systems. In particular, PHM can provide crucial insight into reliability and safety design improvements for developing technologies where historical performance and failure data are limited. This is the case of hydrogen fueling and storage technologies. This work presents a high-level approach for designing data-driven PHM applications for bulk liquid hydrogen (LH2) storage systems for hydrogen fueling stations. This paper addresses core aspects of the design, development, and implementation of data-driven PHM applications that can improve the reliability assessment of hydrogen components. The analysis focuses on the relationship between data availability and diagnostic/prognostic capabilities; potential challenges; and integration schemes for current risk mitigation measures. We identify potential condition-monitoring data sources for key components in an LH2 storage system, including storage tanks, piping, and pumps. We determine that the short-term goals for the implementation of data-driven models in PHM frameworks in hydrogen systems should focus on developing adequate data collection and analysis strategies, as well as exploring the effect on reliability, safety, and regulations for hydrogen systems.

Creation of Publicly Available Data Sets for Prognostics and Diagnostics Addressing Data Scenarios Relevant to Industrial Applications

For a successful realization of prognostics and health management (PHM), the availability of sufficient run-to-failure data sets is a crucial factor. The sheer number of given data points holds less importance than the full coverage of the potential state space. However, full coverage is a major challenge in most industrial applications. Among other things, high investment and operating costs as well as the long service life of many technical systems make it difficult to acquire complete run-to-failure data sets. Consequently, in industrial applications data sets with specific deficiencies are frequently encountered. The development of appropriate methods to address such data scenarios is a fundamental research issue. Therefore, the purpose of this paper is to provide facilitation for this research. Accordingly, the paper starts by specifying the value and availability of data in PHM. Subsequently, criteria for characterizing data sets are defined independent of the actual PHM application. The criteria are used to identify typical data scenarios with specific deficiencies that possess significant relevance for industrial applications. Thereafter, the most comprehensive overview of data sets suitable for PHM and currently publicly accessible is provided. Thereby, not all previously identified data scenarios with their specific deficiencies are addressed by at least one data set. A program is established for the aforementioned facilitation of further research. One objective of the program is to create data sets reflecting these data scenarios using a test bench. First, possible applications and their degradation processes to be studied on the test bench are briefly characterized. Thereby, the final decision to select filtration as a test bench application is argued. Subsequently, the test bench created is introduced, including a description of the functional concept, pneumatic layout and components involved, as well as the filter media and test dusts employed. Typical run-to-failure trajectories are illustrated. Thereafter, the data set published under the name Preventive to Predictive Maintenance is presented. Additionally, a schedule for future releases of data sets on further industry-relevant data scenarios is sketched.

Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management.

Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.

Requirements and Data Integrity Considerations for Diagnostics Testbeds

The process of generating high quality data for the test and evaluation of diagnostic and prognostic algorithms is still of high importance to the Prognostics and Health Management (PHM) research community. To support these efforts a testbed has been designed, manufactured and commissioned. It has specifically been designed in order to replicate several component degradation faults with high accuracy and high repeatability. This paper documents the design, requirements and the data integrity elements of this benchmark hydraulic system. This document consolidates the process of designing diagnostics testbeds as at present there is a lack of literature on how diagnostics testbeds should be built and is intended to serve as a starting point and quick reference guide for engineers and researchers intending to design and develop a testbed to test and validate PHM applications. The first part of this paper highlights design requirements for all the design aspects for such testbeds with great consideration for industry standards and best practices covering the achievement of electromagnetic compatibility (EMC) and noise mitigation, as well as operators’ safety and equipment protection. The second part of the paper put great emphasis on data integrity elements of the data generated by this testbed (describing the system under healthy and faulty conditions) before it is actually used for system characterization or by diagnostics and prognostics algorithms.

Towards an adapted PHM approach: Data quality requirements methodology for fault detection applications

Increasingly, extracting knowledge from data has become an important task in organizations for performance improvements. To accomplish this task, data-driven Prognostics and Health Management (PHM) is introduced as an asset performance management framework for data management and knowledge extraction. However, acquired data come generally with quality issues that affect the PHM process. In this context, data quality problems in the PHM context still an understudied domain. Indeed, the quality of the used data, their quantification, their improvement techniques and their adequacy to the desired PHM tasks are marginalized in the majority of studies. Moreover, many PHM applications are based on the development of very sophisticated data analysis algorithms without taking into account the adaptability of the used data to the fixed objectives. This paper aims to propose a set of data quality requirements for PHM applications and in particular for the fault detection task. The conducted developments in this study are applied to Scoder enterprise, which is a French SME. The feedback on the first results is reported and discussed.

A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships

The maritime industry widely expects to have autonomous and semiautonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient, and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration toward the PHM based on DL in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology.

A Review on Deep Learning Applications in Prognostics and Health Management

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture

In recent years, research has proposed several deep learning (DL) approaches to providing reliable remaining useful life (RUL) predictions in Prognostics and Health Management (PHM) applications. Although supervised DL techniques, such as Convolutional Neural Network and Long-Short Term Memory, have outperformed traditional prognosis algorithms, they are still dependent on large labeled training datasets. With respect to real-life PHM applications, high-quality labeled training data might be both challenging and time-consuming to acquire. Alternatively, unsupervised DL techniques introduce an initial pre-training stage to extract degradation related features from raw unlabeled training data automatically. Thus, the combination of unsupervised and supervised (semi-supervised) learning has the potential to provide high RUL prediction accuracy even with reduced amounts of labeled training data. This paper investigates the effect of unsupervised pre-training in RUL predictions utilizing a semi-supervised setup. Additionally, a Genetic Algorithm (GA) approach is applied in order to tune the diverse amount of hyper-parameters in the training procedure. The advantages of the proposed semi-supervised setup have been verified on the popular C-MAPSS dataset. The experimental study, compares this approach to purely supervised training, both when the training data is completely labeled and when the labeled training data is reduced, and to the most robust results in the literature. The results suggest that unsupervised pre-training is a promising feature in RUL predictions subjected to multiple operating conditions and fault modes.

Data-driven Application of PHM to Asset Strategies

There are many benefits from implementing a prognostics and health management (PHM) initiative in an industrial facility, such as realizing potentials from reducing unplanned downtime and increased asset efficiency. Many industrial companies would like to take advantage of PHM technologies and algorithms to meet their business objectives, but identifying how to get started can be a daunting challenge. The classical approach is to begin with a Reliability Centered Maintenance (RCM) program supported by failure modes and effects analysis (FMEA) where all possible failure modes, their risks, and mitigating actions are evaluated in the context of asset function. In this framework, application of PHM technologies is viewed as a maintenance strategy effective at mitigating certain failure modes in specific cases that are both feasible and costeffective. However, there are many challenges and limitations to traditional RCM where data-driven analytics embedded in these work processes can help overcome and/or automate. On the other hand, the use of data-driven approaches introduces new challenges surrounding available data, data quality, and identifying numerical methods that are scalable across large datasets. In this paper, we present a case study applied to historical maintenance data for identifying and prioritizing where to start a PHM initiative, and discuss the work processes and various challenges encountered when embedding data analytics in classical reliability approaches.

Sequential Monte Carlo: Enabling Real-time and High-fidelity Prognostics

Uncertainty quantification and propagation form the foundation of a prognostics and health management (PHM) system. Particle filters have proven to be a valuable tool for this reason but are generally restricted to state-space damage models and lack a natural approach for quantifying model parameter uncertainty. Both of these issues tend to inhibit the real-world application of PHM. While Markov chain Monte Carlo (MCMC) sampling methods avoid some of these restrictions, they are also inherently serial, and, thus, MCMC can become intractable as model fidelity increases. Over the past two decades, sequential Monte Carlo (SMC) methods, of which the particle filter is a special case, have been adapted to sample from a single, static posterior distribution, eliminating the state-space requirement and providing an alternative to MCMC. Additionally, SMC samplers of this type can be run in parallel, resulting in drastic reductions in computation time. In this work, a potential path toward real-time, highfidelity prognostics using a combination of surrogate modeling and a parallel SMC sampler is explored. The use of SMC samplers to enable tractable parameter estimation for full-fidelity (i.e., non-surrogate-assisted) damage models is also discussed. Both of these topics are studied in the context of fatigue crack growth in a geometrically complex, metallic specimen subjected to variable amplitude loading.

A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems

Prognostics and health management (PHM) has emerged as an intelligent solution to improve the availability of manufacturing systems. PHM consists of system health monitoring, feature extraction, fault diagnosis, and fault prognosis through remaining useful life estimation. However, the application of PHM to manufacturing systems is challenging because systems have become more complex and uncertain. In particular, small and medium-sized enterprises have difficulty in applying PHM due to the lack of internal expertise, time and resources for research and development. The objective of this paper is to develop a framework to provide a readily usable and accessible guideline for PHM application to manufacturing systems. A survey was performed to gather the current practices in dealing with system failures and maintenance strategies in the field. A framework was developed for giving a guideline for PHM application based on common core modules across manufacturing systems and their kinds with respect to the amount of available data and domain knowledge. A reference table was developed to track the PHM techniques for feature extraction, fault diagnosis, and fault prognosis. Finally, fault prognosis of a system was conducted as a case study, following the framework and the reference table to verify its practical use.

Investigating Novel Approaches to Overcome Data Deficiency in Prognostics and Health Management

Recently, the prognostics and health management (PHM) is becoming more important in many industries since it enhances reliability and efficiency during the operation. Among the several issues in the current PHM research, it may be of no doubt that the data deficiency of field data may be one of the most challenges, making the PHM applications difficult in the real industry. Performance of prognostics requires large amount of run-to-fail data, which is however not easy due to the high cost or inability to run until failure under real operation. This is overcome by exploiting two resources in terms of data: 1) accelerated life data at laboratory 2) real data with suspension at field. In this study, a new algorithm is developed that combines these two data sources for better prognostics.

Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications

Prognostics and Health Management (PHM) is an integrated technique for improving the availability and efficiency of high-value industry equipment and reducing the maintenance cost. One of the most challenging problems in PHM is how to effectively process the raw monitoring signal into the information-rich features that are readable enough for PHM modeling. In this paper, we propose an integrated hierarchical learning framework, which is capable to perform the unsupervised feature learning, diagnostics and prognostics modeling together. The proposed method is based on Auto-Encoders (trained by considering the L1-norm penalty) and Extreme Learning Machines (trained by considering the L2-norm penalty). The proposed method is applied on two different case studies considering the diagnostics of motor bearings and prognostics of turbofan engines, also the performances are compared with other commonly applied PHM approaches and machine learning tools. The obtained results demonstrate the superiority of the proposed method, especially the ability of extracting the relevant features from the non-informative and noisy signals and maintaining their efficiencies.