Condition Monitoring Methods for Large, Low-speed Bearings

In all industrial production plants, well-functioning machines and systems are required for sustained and safe operation. However, asset performance degrades over time and may lead to reduced effiency, poor product quality, secondary damage to other assets or even complete failure and unplanned downtime of critical systems. Besides the potential safety hazards from machine failure, the economic consequences are large, particularly in offshore applications where repairs are difficult. This thesis focuses on large, low-speed rolling element bearings, concretized by the main swivel bearing of an offshore drilling machine. Surveys have shown that bearing failure in drilling machines is a major cause of rig downtime. Bearings have a finite lifetime, which can be estimated using formulas supplied by the bearing manufacturer. Premature failure may still occur as a result of irregularities in operating conditions and use, lubrication, mounting, contamination, or external environmental factors. On the contrary, a bearing may also exceed the expected lifetime. Compared to smaller bearings, historical failure data from large, low-speed machinery is rare. Due to the high cost of maintenance and repairs, the preferred maintenance arrangement is often condition based. Vibration measurements with accelerometers is the most common data acquisition technique. However, vibration based condition monitoring of large, low-speed bearings is challenging, due to non-stationary operating conditions, low kinetic energy and increased distance from fault to transducer. On the sensor side, this project has also investigated the usage of acoustic emission sensors for condition monitoring purposes. Roller end damage is identified as a failure mode of interest in tapered axial bearings. Early stage abrasive wear has been observed on bearings in drilling machines. The failure mode is currently only detectable upon visual inspection and potentially through wear debris in the bearing lubricant. In this thesis, multiple machine learning algorithms are developed and applied to handle the challenges of fault detection in large, low-speed bearings with little or no historical data and unknown fault signatures. The feasibility of transfer learning is demonstrated, as an approach to speed up implementation of automated fault detection systems when historical failure data is available. Variational autoencoders are proposed as a method for unsupervised dimensionality reduction and feature extraction, being useful for obtaining a health indicator with a statistical anomaly detection threshold. Data is collected from numerous experiments throughout the project. Most notably, a test was performed on a real offshore drilling machine with roller end wear in the bearing. To replicate this failure mode and aid development of condition monitoring methods, an axial bearing test rig has been designed and built as a part of the project. An overview of all experiments, methods and results are given in the thesis, with details covered in the appended papers.publishedVersio

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

Development of Advanced Techniques For Gear Wear Monitoring and Prediction

Gears are widely used in industrial machinery, and gear failure is a main cause of machine failure. Since gear wear is often the initial stage of gear failure, its monitoring and prediction are key to minimising machine downtime, maintenance costs, and safety risks. However, existing gear wear monitoring and prediction techniques face some ongoing technical challenges, including providing direct wear information and wear assessment and prediction in a cost-effective and efficient manner. To tackle the challenges, this research aims to develop a set of advanced techniques for gear wear monitoring and prediction. The four objectives of the research and their corresponding methodologies and outcomes are summarised as follow. (a) To develop a method to obtain direct and comprehensive wear information without disassembling the gearbox. This objective was realised by combining surface replication with image analysis, allowing easy acquisition of high-resolution mould images showing wear evolution on a tooth flank. (b) To investigate the relationship between the features of worn gear surfaces and those of wear debris. To further understand the role of wear debris analysis in wear assessment, a study on various features of macropits and wear particles in the same fatigue process was conducted and provided new insights into gear pitting and its monitoring. (c) To develop an automated system for gear wear assessment. Deep learning models were developed to identify wear mechanisms and severities using gear mould images and wear debris images. High classification accuracies were achieved, and comparisons between the two image sources were made. (d) To develop a gear wear prediction model using direct wear information. A deep generative model was developed and trained on time series of gear mould images. Tests showed that the model using the state-of-the-art AI technology can generate realistic and accurate predictions. Overall, this research addressed the main limitations of existing methods and provided a direct and evidence-based tool for monitoring and predicting gear wear. Its specific contributions include a new moulding-imaging method for monitoring gear wear evolution, a detailed comparison between worn gear surfaces and wear debris in a wear process, and AI and image-based gear wear assessment and prediction models for the first time. The techniques could be performed during regular inspections of machines and used with online methods for increased robustness

Data modelling and Remaining Useful Life estimation of rolls in a steel making cold rolling process

The economic cost of roll refurbishment in the steel-making industry is considerable. In a cold rolling mill, wear and damage of rolls disrupt the industrial environment, so it is critical to predict the remaining useful life early and change the roll without causing disruption to the manufacturing process. However, since cold rolling is a complex process affected by multiple variables which are operated in adverse conditions, it is very challenging to mathematically analyse the roll wear and failure. For this reason, in the present paper, a data-driven solution is proposed to predict the correct time for changing individual rolls. To develop an accurate predictive model, several datasets containing high-resolution production data and roll refurbishment data collected from a UK based steel plant have been acquired and processed in a way that the roll wear is modelled as a Remaining Useful Life (RUL) problem, where the number of coils that a roll is able to process is viewed as the remaining cycles. Then hybrid deep learning models are used to predict the Remaining Useful Life of rolls used in steel making. This novel data-driven approach achieves high prediction accuracy and has been validated on a real-world dataset. The proposed approach not only helps avoiding early failure but also can serve as a critical step towards the design of an optimal, automated maintenance schedule for the roll management

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions and work-orders written by domain experts are increasingly digitised in modern condition monitoring systems, for example in the context of rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severities exists as technical language annotations in industrial datasets. Furthermore, recent advances in natural language processing enable weakly supervised model optimisation using natural language annotations, most notably in the form of natural language supervision (NLS). This creates a timely opportunity to develop technical language supervision (TLS) solutions for IFD systems grounded in industrial data, for example as a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample generalisation. We surveyed the literature and identify a considerable improvement in the maturity of NLS over the last two years, facilitating applications beyond natural language; a rapid development of weak supervision methods; and transfer learning as a current trend in IFD which can benefit from these developments. Finally we describe a general framework for TLS and implement a TLS case study based on Sentence-BERT and contrastive learning based zero-shot inference on annotated industry data

Intelligent Condition Monitoring and Prognostic Methods with Applications to Dynamic Seals in the Oil & Gas Industry

The capital-intensive oil & gas industry invests billions of dollars in equipment annually and it is important to keep the equipment in top operating condition to help maintain efficient process operations and improve the rate of return by predicting failures before incidents. Digitalization has taken over the world with advances in sensor technology, wireless communication and computational capabilities, however oil & gas industry has not taken full advantage of this despite being technology centric. Dynamic seals are a vital part of reciprocating and rotary equipment such as compressor, pumps, engines, etc. and are considered most frequently failing component. Polymeric seals are increasingly complex and non-linear in behavior and have been the research of interest since 1950s. Most of the prognostic studies on seals are physics-based and requires direct estimation of different physical parameters to assess the degradation of seals, which are often difficult to obtain during operation. Another feasible approach to predict the failure is from performance related sensor data and is termed as data-driven prognostics. The offline phase of this approach is where the performance related data from the component of interest are acquired, pre-processed and artificial intelligence tools or statistical methods are used to model the degradation of a system. The developed models are then deployed online for a real-time condition monitoring. There is a lack of research on the data-driven based tools and methods for dynamic seal prognosis. The primary goal in this dissertation is to develop offline data-driven intelligent condition monitoring and prognostic methods for two types of dynamic seals used in the oil & gas industry, to avoid fatal breakdown of rotary and reciprocating equipment. Accordingly, the interest in this dissertation lies in developing models to effectively evaluate and classify the running condition of rotary seals; assess the progression of degradation from its incipient to failure and to estimate the remaining useful life (RUL) of reciprocating seals. First, a data-driven prognostic framework is developed to classify the running condition of rotary seals. An accelerated aging and testing procedure simulating rotary seal operation in oil field is developed to capture the behavior of seals through their cycle of operation until failure. The diagnostic capability of torque, leakage and vibration signal in differentiating the health states of rotary seals using experiments are compared. Since the key features that differentiate the health condition of rotary seals are unknown, an extensive feature extraction in time and frequency domain is carried out and a wrapper-based feature selection approach is used to select relevant features, with Multilayer Perceptron neural network utilized as classification technique. The proposed approach has shown that features extracted from torque and leakage lack a better discriminating power on its own, in classifying the running condition of seals throughout its service life. The classifier built using optimal set of features from torque and leakage collectively has resulted in a high classification accuracy when compared to random forest and logistic regression, even for the data collected at a different operating condition. Second, a data-driven approach to predict the degradation process of reciprocating seals based on friction force signal using a hybrid Particle Swarm Optimization - Support Vector Machine is presented. There is little to no knowledge on the feature that reflects the degradation of reciprocating seals and on the application of SVM in predicting the future running condition of polymeric components such as seals. Controlled run-to-failure experiments are designed and performed, and data collected from a dedicated experimental set-up is used to develop the proposed approach. A degradation feature with high monotonicity is used as an indicator of seal degradation. The pseudo nearest neighbor is used to determine the essential number of inputs for forecasting the future trend. The most challenging aspect of tuning parameters in SVM is framed in terms of an optimization problem aimed at minimizing the prediction error. The results indicate the effectiveness and better accuracy of the proposed approach when compared to GA-SVM and XGBoost. Finally, a deep neural network-based approach for estimating remaining useful life of reciprocating seals, using force and leakage signals is presented. Time domain and frequency domain statistical features are extracted from the measurements. An ideal prognostic feature should be well correlated with degradation time, monotonically increasing or decreasing and robust to outliers. The identified metrics namely: monotonicity, correlation and robustness are used to evaluate the goodness of extracted features. Each of the three metric carries a relative importance in the RUL estimation and a weighted linear combination of the metrics are used to rank and select the best set of prognostic features. The redundancy in the selected features is eliminated using Kelley-Gardner-Sutcliffe penalty function-based correlation-clustering algorithm to select a representative feature from each of the clusters. Finally, RUL estimation is modeled using a deep neural network model. Run-to-failure data collected from a reciprocating set-up was used to validate this approach and the findings show that the proposed approach can improve the accuracy of RUL prediction when compared to PSO-SVM and XGBoost regression. This research has important contribution and implications to rotary and reciprocating seal domain in utilizing sensors along with machine learning algorithms in assessing the health state and prognosis of seals without any direct measurements. This research has paved the way to move from a traditional fail-and-fix to predict-and-prevent approach in maintenance of seals. The findings of this research are foundational for developing an online degradation assessment platform which can remotely monitor the performance degradation of seals and provide action recommendations on maintenance decisions. This would be of great interest to customers and oil field operators to improve equipment utilization, control maintenance cost by enabling just-in-time maintenance and increase rate of return on equipment by predicting failures before incidents

A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels