Machinery health prognostics: A systematic review from data acquisition to RUL prediction

The detection, diagnostic, and prognostic of bearing degradation play a key role in increasing the reliability and safety of electrical machines, especially in key industrial sectors. This paper presents a new approach that combines the Hilbert-Huang transform (HHT), the support vector machine (SVM), and the support vector regression (SVR) for the monitoring of ball bearings. The proposed approach uses the HHT to extract new heath indicators from stationary/nonstationary vibration signals able to tack the degradation of the critical components of bearings. The degradation states are detected by a supervised classification technique called SVM, and the fault diagnostic is given by analyzing the extracted health indicators. The estimation of the remaining useful life is obtained by a one-step time-series prediction based on SVR. A set of experimental data collected from degraded bearings is used to validate the proposed approach. The experimental results show that the use of the HHT, the SVM, and the SVR is a suitable strategy to improve the detection, diagnostic, and prognostic of bearing degradation.

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Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression

The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years [1].

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Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques

Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.

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Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

The remaining useful life (RUL) prediction of rolling element bearings has attracted substantial attention recently due to its importance for the bearing health management. The exponential model is one of the most widely used methods for RUL prediction of rolling element bearings. However, two shortcomings exist in the exponential model: 1) the first predicting time (FPT) is selected subjectively; and 2) random errors of the stochastic process decrease the prediction accuracy. To deal with these two shortcomings, an improved exponential model is proposed in this paper. In the improved model, an adaptive FPT selection approach is established based on the $3\sigma$ interval, and particle filtering is utilized to reduce random errors of the stochastic process. In order to demonstrate the effectiveness of the improved model, a simulation and four tests of bearing degradation processes are utilized for the RUL prediction. The results show that the improved model is able to select an appropriate FPT and reduce random errors of the stochastic process. Consequently, it performs better in the RUL prediction of rolling element bearings than the original exponential model.

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the system's degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.

A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

Highlights? .NET framework as foundation of the software architecture. ? Probabilistic method called particle filtering using Bayesian estimates perform diagnostics and prognostics. ? Real world applications show the advantages of the architecture. Complex engineering systems, such as aircraft, industrial processes, and transportation systems, are experiencing a paradigm shift in the way they are operated and maintained. Instead of traditional scheduled or breakdown maintenance practices, they are maintained on the basis of their current state/condition. Condition-Based Maintenance (CBM) is becoming the preferred practice since it improves significantly the reliability, safety and availability of these critical systems. CBM enabling technologies include sensing and monitoring, information processing, fault diagnosis and failure prognosis algorithms that are capable of detecting accurately and in a timely manner incipient failures and predicting the remaining useful life of failing components. If such technologies are to be implemented on-line and in real-time, it is essential that an integrating system architecture be developed that possesses features of modularity, flexibility and interoperability while exhibiting attributes of computational efficiency for both on-line and off-line applications. This paper presents a .NET framework as the integrating software platform linking all constituent modules of the fault diagnosis and failure prognosis architecture. The inherent characteristics of the .NET framework provide the proposed system with a generic architecture for fault diagnosis and failure prognosis for a variety of applications. Functioning as data processing, feature extraction, fault diagnosis and failure prognosis, the corresponding modules in the system are built as .NET components that are developed separately and independently in any of the .NET languages. With the use of Bayesian estimation theory, a generic particle-filtering-based framework is integrated in the system for fault diagnosis and failure prognosis. The system is tested in two different applications-bearing spalling fault diagnosis and failure prognosis and brushless DC motor turn-to-turn winding fault diagnosis. The results suggest that the system is capable of meeting performance requirements specified by both the developer and the user for a variety of engineering systems.

An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems

This paper introduces a methodology to detect as early as possible with specified degree of confidence and prescribed false alarm rate an anomaly or novelty (incipient failure) associated with critical components/subsystems of an engineered system that is configured to monitor continuously its health status. Innovative features of the enabling technologies include a Bayesian estimation framework, called particle filtering, that employs features or condition indicators derived from sensor data in combination with simple models of the systempsilas degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme provides the probability of abnormal condition and the probability of false alarm. The presence of an anomaly is confirmed for a given confidence level. The efficacy of the proposed anomaly detection architecture is illustrated with test data acquired from components typically found on aircraft and monitored via a test rig appropriately instrumented.

Anomaly detection: A robust approach to detection of unanticipated faults

Accurate and reliable fault diagnosis and prognosis of safety or mission critical components/ subsystems in complex engineering systems present major challenges to the Condition-Based Maintenance (CBM) or Prognostic and Health Management (PHM) designer. A crucial step in the development of CBM/PHM strategies relates to the designer’s ability to understand and model the incipient failure or fault modes and mechanisms. A single fault growth model might not be often capable to capture a sequence of fault behaviors. Consider, for example, a rolling element bearing as a critical component of rotating machinery. The bearing may begin to corrode under certain operating conditions and, in parallel or sequentially, may be spalling and eventually, cracking. For accurate model-based fault diagnosis and failure prognosis, therefore, it is essential that fault progression models be developed to represent these evolving behaviors. This paper introduces an approach to multi-fault modeling with an application to a rolling element bearing of a helicopter’s oil cooler. A simple and cost-effective on-line parameter adaptation solution is introduced to improve the performance of modeling. Finally, a series of experiments for different fault modes are presented to verify the proposed solution.

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A Multi-Fault Modeling Approach for Fault Diagnosis and Failure Prognosis of Engineering Systems

The successful implementation of fault diagnosis and failure prognosis algorithms to safety critical systems requires the definitions and applications of mathematically rigorous modules. These modules, including data preprocessing, feature extraction, diagnostic and prognostic algorithms, performance metrics definition, and a fault progression model, form an integrated architecture for system health monitoring and management. In these modules, the fault progression model is critical to detection of incipient failures as early as possible with predefined specifications and prediction of the system's remaining useful life accurately and precisely. This paper considers an oil cooler bearing of a helicopter and proposes a methodology for fault detection and failure prognosis, in which data pre-processing, feature extraction and fault progression modeling are discussed in detail. Experimental results are presented to verify the proposed methodology and fault progression model.

Chris Sconyers

Papers

A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems

Anomaly detection: A robust approach to detection of unanticipated faults

A Multi-Fault Modeling Approach for Fault Diagnosis and Failure Prognosis of Engineering Systems

Fault progression modeling: An application to bearing diagnosis and prognosis