A hybrid prognostics approach for motorized spindle-tool holder remaining useful life prediction

The quality and efficiency of high-speed machining are restricted by the matching performance of the motorized spindle-tool holder. In high speed cutting process, the mating surface is subjected to alternating torque, repeated clamping wear and centrifugal force, which results in serious degradation of mating performance. Therefore, for the purpose of the optimum maintenance time, periodic evaluation and prediction of remaining useful life (RUL) should be carried out. Firstly, the mapping model between the current of the motorized spindle and matching performance was extracted, and the degradation characteristics of spindle-tool holder were emphatically analyzed. After the original current is de-noised by an adaptive threshold function, the extent of degradation was identified by the amplitudes of wavelet packet entropy. A hybrid prognostics combining Relevance Vector Machine (RVM) i.e. AI-model with power regression i.e. statistical model was proposed to predict the RUL. Finally, the proposed scheme was verified based on a motorized spindle reliability test platform. The experimental results show that the current signal processing method based on wavelet packet and entropy can reflect the change of the degradation characteristics sensitively. Compared with other two similar models, the hybrid model proposed can accurately predict the RUL. This model is suitable for complex and high reliability equipment when Condition Monitoring (CM) data is scarcer

Reciprocating compressor prognostics

Reciprocating compressors are vital components in oil and gas industry though their maintenance cost can be high. The valves are considered the most frequent failing part accounting for almost half the maintenance cost. Condition Based Maintenance and Prognostics and Health Management which is based on diagnostics and prognostics principles can assist towards reducing cost and downtime while increasing safety and availability by offering a proactive means for scheduling maintenance. Although diagnostics is an established field for reciprocating compressors, there is limited information regarding prognostics, particularly given the nature of failures can be instantaneous. This paper compares several prognostics methods (multiple liner regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)) using valve failure data from an operating industrial compressor. Moreover, a variation about Remaining Useful Life (RUL) estimation process based on KNNR is proposed along with an ensemble method combining the output of all aforementioned algorithms. In conclusion it is showed that even when RUL is relatively short given the instantaneous failure mode, good estimates are feasible using the proposed methods

Bearing Incipient Fault Detection, Diagnosis, and Unsupervised Prognosis with Failure Thresholding

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 윤병동.Bearings are core components in rotating machines. Thus, early detection of faults and accurate prediction of a machines health state is highly desirable throughout the total lifecycle of a bearing. Rolling element bearing failure is one of the critical causes of breakdowns in rotating machinerythese types of failures are common in mechanical systems as well. Such failures can be catastrophic and can result in costly downtime. Particularly in industrial fields, minimization of downtime is critical. Thus, health monitoring of rotating machinery during operation is the focus of significant research interest. Accurate bearing health prediction is needed for these settings. There remains a need for health state prediction that can be accomplished in real-time, without future data. Therefore, a data-driven and real-time algorithm for bearing health monitoring is suggested in this thesis. The research objectives pursued to improve the bearing PHM framework include 1) full-time health monitoring, 2) definition of a failure threshold for rolling elements in general bearings, and 3) life prediction in real-time and in unsupervised situations. To classify the health state of bearings for detection of incipient faults and fault points, the Mahalanobis Distance is applied. For life prediction, previous researchers have experienced severe problems, particularly when the life prediction required analytic assumptions as a prerequisite, for example, those emerged at Particle Filters. To solve this problem, the research outlined in this paper suggests a new model and a threshold decision method that enables prediction of the Remaining Useful Life in real time (i.e., in unsupervised situations). This thesis is organized as follows. Section 1 provides an introduction, including the research motivation and an overview of the research objectives. Next, in Section 2, methodologies for detection of incipient anomalies, fault diagnosis, and failure prognosis are explained, along with a suggested definition and a trend projection model. Then, Sections 3 and 4 validate the suggested threshold and model using data acquired from Schaeffler Korea and Seoul National University, respectively. Finally, Chapter 5 concludes this thesis with a summary of the research contributions and suggestions for future work.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives 2 1.3 Thesis Layout 5 Chapter 2. Methodology 6 2.1 Bearing Overall PHM Flowchart 6 2.2 Preprocessing and Feature Extraction 9 2.3 Bound Decision for Incipient Anomaly and Fault 12 2.4 Incipient Anomaly Detection 16 2.5 Fault Diagnosis 20 2.6 Failure Prognosis 23 2.6.1 Background 23 2.6.2 Trend Projection 24 2.6.3 Threshold Decision 25 Chapter 3. Case Study 1: Schaeffler Bearing Data 32 3.1 Data Description 32 3.2 Prognostic Result 35 Chapter 4. Case Study 2: SNU Bearing Testbed Data 37 4.1 Data Description 37 4.2 Prognostic Result 39 Chapter 5. Conclusion 49 5.1 Conclusion and Contribution 49 5.2 Future Work 50 Bibliography 51 Abstract in Korean 53Maste

Instantaneous failure mode remaining useful life estimation using non-uniformly sampled measurements from a reciprocating compressor valve failure

One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) , which is founded on the principles of diagnostics, and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. The SOM technique is employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements

Reciprocating compressors are critical components in the oil and gas sector, though their maintenance cost is known to be relatively high. Compressor valves are the weakest component, being the most frequent failure mode, accounting for almost half the maintenance cost. One of the major targets in industry is minimisation of downtime and cost, while maximising availability and safety of a machine, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the diagnostics and prognostics principles, is a step towards this direction as it offers a proactive means for scheduling maintenance. Despite the fact that diagnostics is an established area for reciprocating compressors, to date there is limited information in the open literature regarding prognostics, especially given the nature of failures can be instantaneous. This work presents an analysis of prognostic performance of several methods (multiple linear regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and variability, using actual temperature only valve failure data, an instantaneous failure mode, from an operating industrial compressor. Furthermore, a variation for Remaining Useful Life (RUL) estimation based on KNNR, along with an ensemble technique merging the results of all aforementioned methods are proposed. Prior to analysis, principal components analysis and statistical process control were employed to create !! and ! metrics, which were proposed to be used as health indicators reflecting degradation process of the valve failure mode and are proposed to be used for direct RUL estimation for the first time. Results demonstrated that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy

A new dynamic predictive maintenance framework using deep learning for failure prognostics

In Prognostic Health and Management (PHM) literature, the predictive maintenance studies can be classified into two groups. The first group focuses on the prognostics step but does not consider the maintenance decisions. The second group addresses the maintenance optimization question based on the assumptions that the prognostics information or the degradation models of the system are already known. However, none of the two groups provides a complete framework (from data-driven prognostics to maintenance decisions) investigating the impact of the imperfect prognostics on maintenance decision. Therefore, this paper aims to fill this gap of literature. It presents a novel dynamic predicive maintenance framework based on sensor measurements. In this framework, the prognostics step, based on the Long Short-Term Memory network, is oriented towards the requirements of operation planners. It provides the probabilities that the system can fail in different time horizons to decide the moment for preparing and performing maintenance activities. The proposed framework is validated on a real application case study. Its performance is highlighted when compared with two benchmark maintenance policies: classical periodic and ideal predicted maintenance. In addition, the impact of the imperfect prognostics information on maintenance decisions is discussed in this paper

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

Toward Predicting Global Seismicity of the Earth using Machine Learning Techniques and Solar Activity Data

An earthquake is one of the deadliest natural disasters. Forecasting an earthquake is a challenging task since natural causes such as movement of tectonic plates, volcanic eruptions, rainfall, and tidal stress all play an important part in earthquakes. Earthquakes can also be caused by human beings, such as mining, dams, nuclear bomb testing, etc. Solar activity has also been suggested as a possible cause of earthquakes. Solar activity and earthquakes occur in different parts of the solar system, on the Sun’s surface and the Earth’s surface, separated by a huge distance. However, scientists have been trying to figure out if there are any links between these two seemingly unrelated occurrences since the 19th century. In this study, four machine learning algorithms k-nearest neighbour, support vector regression, random forest regression, and Long Short-Term Memory network were applied to understand if there is a relationship between solar activity and earthquakes. The study employed three types of solar activity: sunspot number, solar wind, and solar flares, as well as worldwide earthquake frequencies that ranged in magnitude and depth. The study's findings imply that the Long Short-Term Memory network model predicts earthquakes more accurately than other models. There's a chance that earthquakes are influenced by solar activity. Earthquakes with a magnitude less than 5.5 are more linked to solar activity than earthquakes with a magnitude equal to or higher than 5.5. Solar activity has a bigger impact on earthquakes of lower depths