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

Pembangunan model penentuan keperluan perumahan kajian kes: Johor Bahru, Malaysia

Perumahan merupakan satu komponen penting dalam pembangunan ekonomi di mana ia telah menjadi dasar kerajaan untuk menyediakan rumah bagi setiap rakyat. Rancangan Malaysia terdahulu telah cuba merancang bagi merealisasikan dasar ini. Walaupun anggaran keperluan perumahan dibuat di bawah Rancangan Malaysia, namun anggaran tersebut tidak membayangkan keperluan sebenar pembeli dan penyewa rumah di Malaysia. Negara-negara maju telah menggunakan pelbagai model dalam menentukan keperluan perumahan. Namun begitu, model-model tersebut tidak sesuai digunakan di Malaysia kerana data yang terhad. Kajian ini memfokuskan kepada dua objektif iaitu, mengenal pasti model dan faktor yang signifikan bagi menentukan keperluan perumahan, dan kedua menghasilkan model penentuan keperluan perumahan di Malaysia. Skop kajian ini tertumpu kepada pembeli dan penyewa rumah di Daerah Johor Bahru yang dipilih melalui kaedah pesampelan kelompok pelbagai peringkat. Data diperolehi melalui borang kaji selidik dan dianalisis menggunakan pendekatan kuantitatif. Analisis statistik deskriptif digunakan bagi menghuraikan taburan kekerapan, peratus, min, dan sisihan piawai manakala statistik inferensi iaitu ujian Korelasi Pearson dan Regresi Pelbagai digunakan untuk pembentukan model. Dengan menggunakan kaedah Enter, satu model yang signifikan dapat dihasilkan (F4,178 = 353.699 p < 0.05. Adjusted R square = .886) yang signifikan terhadap dua faktor utama iaitu demografi dan kemampuan. Model yang dihasilkan bagi kajian ini adalah General Linear Model. Model ini dapat digunakan bagi menentukan keperluan perumahan di Johor Bahru. Ia juga berfungsi sebagai alat penting dalam perancangan sektor perumahan pada masa hadapan di Malaysia

Combined misalignments in spur gear transmission systems: a semi-empirical approach

Un área inexplorada en el diseño de sistemas de transmisión de engranajes es el estudio de los efectos de desalineamientos combinados en las medidas de vibración. Actualmente, las investigaciones se centran en desalineamientos individuales y no combinados los cuales reflejan mejor los escenarios de aplicaciones reales. En esta investigación se analizan los efectos de los desalineamientos combinados en las mediciones de vibración en la base de los rodamientos y en el esfuerzo de flexión de los dientes de engranajes rectos de un sistema de transmisión de una etapa. Se diseñó y construyó un banco de pruebas para generar desalineamientos radiales, axiales y angulares en un sistema de transmisión de engranajes de una etapa. Se evaluaron todas las combinaciones posibles de niveles extremos de desalineamiento para un par de engranajes rectos para identificar tendencias en la respuesta vibratoria. Se desarrolló un modelo teórico del área de contacto proyectada para estudiar la relación entre esta y la respuesta vibratoria. Al analizar el cambio en los espectros, se determinó la influencia de diferentes desalineamientos y sus interacciones en las mediciones de vibración. Finalmente, se desarrolló un modelo híbrido para estimar las aceleraciones en los rodamientos, utilizando un modelo de elementos finitos para determinar el esfuerzo de flexión en los dientes y un modelo analítico para estimar las señales de vibración en los rodamientos. El modelo demostró una alta correlación en comparación con los resultados experimentales, validando su efectividad. Finalmente, se propusieron recomendaciones de diseño considerando las zonas de esfuerzo y vibración de interés.DoctoradoDoctor en Ingeniería Mecánic

Diagnosis of low-speed bearing degradation using acoustic emission techniques

It is widely acknowledged that bearing failures are the primary reason for breakdowns in rotating machinery. These failures are extremely costly, particularly in terms of lost production. Roller bearings are widely used in industrial machinery and need to be maintained in good condition to ensure the continuing efficiency, effectiveness, and profitability of the production process. The research presented here is an investigation of the use of acoustic emission (AE) to monitor bearing conditions at low speeds. Many machines, particularly large, expensive machines operate at speeds below 100 rpm, and such machines are important to the industry. However, the overwhelming proportion of studies have investigated the use of AE techniques for condition monitoring of higher-speed machines (typically several hundred rpm, or even higher). Few researchers have investigated the application of these techniques to low-speed machines (<100 rpm), This PhD addressed this omission and has established which, of the available, AE techniques are suitable for the detection of incipient faults and measurement of fault growth in low-speed bearings. The first objective of this research program was to assess the applicability of AE techniques to monitor low-speed bearings. It was found that the measured statistical parameters successfully monitored bearing conditions at low speeds (10-100 rpm). The second objective was to identify which commonly used statistical parameters derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify the onset of a fault in either race. It was found that the change in AE amplitude and AE RMS could identify the presence of a small fault seeded into either the inner or the outer races. However, the severe attenuation of the signal from the inner race meant that, while AE amplitude and RMS could readily identify the incipient fault, kurtosis and the AE counts could not. Thus, more attention needs to be given to analysing the signal from the inner race. The third objective was to identify a measure that would assess the degree of severity of the fault. However, once the defect was established, it was found that of the parameters used only AE RMS was sensitive to defect size. The fourth objective was to assess whether the AE signal is able to detect defects located at either the centre or edge of the outer race of a bearing rotating at low speeds. It is found that all the measured AE parameters had higher values when the defect was seeded in the middle of the outer race, possibly due to the shorter path traversed by the signal between source and sensor which gave a lower attenuation than when the defect was on the edge of the outer race. Moreover, AE can detect the defect at both locations, which confirmed the applicability of the AE to monitor the defects at any location on the outer race

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

Rolling contact fatigue failures in silicon nitride and their detection

The project investigates the feasibility of using sensor-based detection and processing systems to provide a reliable means of monitoring rolling contact fatigue (RCF) wear failures of silicon nitride in hybrid bearings. To fulfil this investigation, a decision was made early in the project to perform a series of hybrid rolling wear tests using a twin disc machine modified for use on hybrid bearing elements.The initial part of the thesis reviews the current understanding of the general wear mechanisms and RCF with a specific focus to determine the appropriate methods for their detection in hybrid bearings. The study focusses on vibration, electrostatic and acoustic emission (AE) techniques and reviews their associated sensing technologies currently deployed with a view of adapting them for use in hybrids. To provide a basis for the adaptation, an understanding of the current sensor data enhancement and feature extraction methods is presented based on a literature review.The second part describes the test equipment, its modifications and instrumentation required to capture and process the vibration, electrostatic and AE signals generated in hybrid elements. These were identified in an initial feasibility test performed on a standard twin disc machine. After a detailed description of the resulting equipment, the thesis describes the calibration tests aimed to provide base data for the development of the signal processing methods.The development of the signal processing techniques is described in detail for each of the sensor types. Time synchronous averaging (TSA) technique is used to identify the location of the signal sources along the surfaces of the specimens and the signals are enhanced by additional filtering techniques.The next part of the thesis describes the main hybrid rolling wear tests; it details the selection of the run parameters and the samples seeded with surface cracks to cover a variety of situations, the method of execution of each test run, and the techniques to analyse the results.The research establishes that two RCF fault types are produced in the silicon nitride rolling element reflecting essentially different mechanisms in their distinct and separate development; i) cracks, progressing into depth and denoted in this study as C-/Ring crack Complex (CRC) and ii) Flaking, progressing primarily on the surface by spalls. Additionally and not reported in the literature, an advanced stage of the CRC fault type composed of multiple and extensive c-cracks is interpreted as the result of induced sliding in these runs. In general, having reached an advanced stage, both CRC and Flaking faults produce significant wear in the steel counterface through abrasion, plastic deformation or 3-body abrasion in at least three possible ways, all of which are described in details

Experimental Evidence of the Speed Variation Effect on SVM Accuracy for Diagnostics of Ball Bearings

In recent years, we have witnessed a considerable increase in scientific papers concerning the condition monitoring of mechanical components by means of machine learning. These techniques are oriented towards the diagnostics of mechanical components. In the same years, the interest of the scientific community in machine diagnostics has moved to the condition monitoring of machinery in non-stationary conditions (i.e., machines working with variable speed profiles or variable loads). Non-stationarity implies more complex signal processing techniques, and a natural consequence is the use of machine learning techniques for data analysis in non-stationary applications. Several papers have studied the machine learning system, but they focus on specific machine learning systems and the selection of the best input array. No paper has considered the dynamics of the system, that is, the influence of how much the speed profile changes during the training and testing steps of a machine learning technique. The aim of this paper is to show the importance of considering the dynamic conditions, taking the condition monitoring of ball bearings in variable speed applications as an example. A commercial support vector machine tool is used, tuning it in constant speed applications and testing it in variable speed conditions. The results show critical issues of machine learning techniques in non-stationary conditions

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Explainable AI for Machine Fault Diagnosis: Understanding Features' Contribution in Machine Learning Models for Industrial Condition Monitoring

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models’ outcomes