Asymmetric HMMs for online ball-bearing health assessments

The degradation of critical components inside large industrial assets, such as ball-bearings, has a negative impact on production facilities, reducing the availability of assets due to an unexpectedly high failure rate. Machine learning- based monitoring systems can estimate the remaining useful life (RUL) of ball-bearings, reducing the downtime by early failure detection. However, traditional approaches for predictive systems require run-to-failure (RTF) data as training data, which in real scenarios can be scarce and expensive to obtain as the expected useful life could be measured in years. Therefore, to overcome the need of RTF, we propose a new methodology based on online novelty detection and asymmetrical hidden Markov models (As-HMM) to work out the health assessment. This new methodology does not require previous RTF data and can adapt to natural degradation of mechanical components over time in data-stream and online environments. As the system is designed to work online within the electrical cabinet of machines it has to be deployed using embedded electronics. Therefore, a performance analysis of As-HMM is presented to detect the strengths and critical points of the algorithm. To validate our approach, we use real life ball-bearing data-sets and compare our methodology with other methodologies where no RTF data is needed and check the advantages in RUL prediction and health monitoring. As a result, we showcase a complete end-to-end solution from the sensor to actionable insights regarding RUL estimation towards maintenance application in real industrial environments.This study was supported partially by the Spanish Ministry of Economy and Competitiveness through the PID2019-109247GB-I00 project and by the Spanish Ministry of Science and Innovation through the RTC2019-006871-7 (DSTREAMS project). Also, by the H2020 IoTwins project (Distributed Digital Twins for industrial SMEs: a big-data platform) funded by the EU under the call ICT-11-2018- 2019, Grant Agreement No. 857191.Peer ReviewedPostprint (author's final draft

Advances in Bearing Lubrication and Thermal Sciences

This reprint focuses on the hot issue of bearing lubrication and thermal analysis, and brings together many cutting-edge studies, such as bearing multi-body dynamics, bearing tribology, new lubrication and heat dissipation structures, bearing self-lubricating materials, thermal analysis of bearing assembly process, bearing service state prediction, etc. The purpose of this reprint is to explore recent developments in bearing thermal mechanisms and lubrication technology, as well as the impact of bearing operating parameters on their lubrication performance and thermal behavior

PROGNOSTIC ALGORITHM DEVELOPMENT FOR PLANT MONITORING AND MAINTENANCE PLANNING

The economic goals in a typical industrial plant are to improve product quality, maximize equipment up-time, reliability, and availability, and minimize spare part inventories and maintenance costs. Modern facilities are comprised of thousands of subsystems with critical unique components. Simple components and more complex engineering systems alike are typically engineered to perform satisfactorily. Their lives can be predicted under normal operation runtime. It should be the same with chronological time lapse from the moment of installation. However, their ages accelerate faster than chronological time lapse if they are operated under unfavorable working conditions, making their remaining life predictions likely not accurate, thus making failure imminent. These components most become more sophisticated and advanced to meet supercritical demands, and unplanned critical failures of any these components can result in costly operation stoppages. Speedy repair costs of failed components during operation can be extremely costly, not only due to the failed component, but also to collateral damage to other components, which can result in significant economic loss, lost production, personal injury, and even loss of life. Today’s marketplace faces global competition, ever-changing customer perception, and evolving demand. Industrial plants are constantly retooling their operations and equipment to act in a supercritical manner, and this is happening amidst the already complex nature of mechanical structures, operational stress, and environmental influence. To address these continuous changes, early fault detection is imperative to accurately predict the Remaining Useful Life (RUL) of machinery to prevent performance degradation and malfunction, which leads to substantial damage. Predicting the RUL of degraded components and putting these components to use will reduce spare part inventories and maintenance and increase reliability, availability, and performance to minimize plant downtime and production loss while enhancing operation safety. The primary purpose of this dissertation is to create an improved prognostic algorithm and methodology to predict the time of machinery failure. Empirical wear models built using historical operating conditions are then used to monitor the RUL of machinery and components. Machinery online monitoring data are used to determine the current health state of components along their life curves

Sensor data-based decision making

Increasing globalization and growing industrial system complexity has amplified the interest in the use of information provided by sensors as a means of improving overall manufacturing system performance and maintainability. However, utilization of sensors can only be effective if the real-time data can be integrated into the necessary business processes, such as production planning, scheduling and execution systems. This integration requires the development of intelligent decision making models that can effectively process the sensor data into information and suggest appropriate actions. To be able to improve the performance of a system, the health of the system also needs to be maintained. In many cases a single sensor type cannot provide sufficient information for complex decision making including diagnostics and prognostics of a system. Therefore, a combination of sensors should be used in an integrated manner in order to achieve desired performance levels. Sensor generated data need to be processed into information through the use of appropriate decision making models in order to improve overall performance. In this dissertation, which is presented as a collection of five journal papers, several reactive and proactive decision making models that utilize data from single and multi-sensor environments are developed. The first paper presents a testbed architecture for Auto-ID systems. An adaptive inventory management model which utilizes real-time RFID data is developed in the second paper. In the third paper, a complete hardware and inventory management solution, which involves the integration of RFID sensors into an extremely low temperature industrial freezer, is presented. The last two papers in the dissertation deal with diagnostic and prognostic decision making models in order to assure the healthy operation of a manufacturing system and its components. In the fourth paper a Mahalanobis-Taguchi System (MTS) based prognostics tool is developed and it is used to estimate the remaining useful life of rolling element bearings using data acquired from vibration sensors. In the final paper, an MTS based prognostics tool is developed for a centrifugal water pump, which fuses information from multiple types of sensors in order to take diagnostic and prognostics decisions for the pump and its components --Abstract, page iv

Mechanical Wear Debris Feature, Detection, and Diagnosis: A Review

Mechanical debris is an important product of friction wear, which is also a crucial approach to know the running status of a machine. Many studies have been conducted on mechanical debris in related fields such as tribology, instrument, and diagnosis. This paper presents a comprehensive review of these studies, which summarizes wear mechanisms (e.g., abrasive wear, fatigue wear, and adhesive wear) and debris features (e.g., concentration (number), size, morphology, and composition), analyzes detection methods principles (e.g., offline: spectrograph and ferrograph, and online: optical method, inductive method, resistive-capacitive method, and acoustic method), reviews developments of online inductive methods, and investigates the progress of debris-based diagnosis. Finally, several notable problems are discussed for further studies. (C) 2017 Chinese Society of Aeronautics and Astronautics

Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System

This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input – output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

A Study on Comparison of Classification Algorithms for Pump Failure Prediction

The reliability of pumps can be compromised by faults, impacting their functionality. Detecting these faults is crucial, and many studies have utilized motor current signals for this purpose. However, as pumps are rotational equipped, vibrations also play a vital role in fault identification. Rising pump failures have led to increased maintenance costs and unavailability, emphasizing the need for cost-effective and dependable machinery operation. This study addresses the imperative challenge of defect classification through the lens of predictive modeling. With a problem statement centered on achieving accurate and efficient identification of defects, this study’s objective is to evaluate the performance of five distinct algorithms: Fine Decision Tree, Medium Decision Tree, Bagged Trees (Ensemble), RUS-Boosted Trees, and Boosted Trees. Leveraging a comprehensive dataset, the study meticulously trained and tested each model, analyzing training accuracy, test accuracy, and Area Under the Curve (AUC) metrics. The results showcase the supremacy of the Fine Decision Tree (91.2% training accuracy, 74% test accuracy, AUC 0.80), the robustness of the Ensemble approach (Bagged Trees with 94.9% training accuracy, 99.9% test accuracy, and AUC 1.00), and the competitiveness of Boosted Trees (89.4% training accuracy, 72.2% test accuracy, AUC 0.79) in defect classification. Notably, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) exhibited comparatively lower performance. Our study contributes valuable insights into the efficacy of these algorithms, guiding practitioners toward optimal model selection for defect classification scenarios. This research lays a foundation for enhanced decision-making in quality control and predictive maintenance, fostering advancements in the realm of defect prediction and classification