Equipment Health Management Research Articles

Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features

Condition monitoring and fault diagnosis play an important role in the health management of mechanical equipment. However, the robust performance of data-driven-based methods with unknown fault inputs remains to be further improved. In this paper, a novel approach of condition monitoring and fault diagnosis is proposed for rolling element bearings based on an improved ensemble empirical mode decomposition (IEEMD), which is able to solve the non-intrinsic mode function problem of EEMD. In this method, IEEMD is applied to process the primordial vibration signals collected from rolling element bearings at first. Then the correlation analysis and data fusion technology are introduced to extract statistical features from these decomposition results of IEEMD. Finally, a complete self-zero space model is constructed for the condition monitoring and fault diagnosis of rolling element bearings. Experiments are implemented on a mechanical fault simulator to demonstrate the reliability and effectiveness of the proposed method. The experimental results show that the proposed method can not only diagnose known faults but also monitor unknown faults with strong robust performance.

Fuzzy clustering of complex equipment maintenance for equipment health management

Fuzzy clustering of complex equipment maintenance for equipment health management

The Maintenance Decision-Making for Long-Term Reserved Equipment Based on the Failure Data

A method is to be put forward to make the maintenance decision for long-term reserved equipment. The schemes of health management is conceived according to the characteristics of the long-term reserved equipment. Then, it is studied how to define the equipment life distributing by failure data. The method is researched to make the maintenance decision based on the store life rule of long-term reserved equipment. The long-term reserved equipment health management platform is designed. The feasibility of this method is demonstrated by an example. It has some reference value towards the health management of the long-term reserved equipment.

Study on the Distributed Detection System for Digital TV Signal Based on Prognostic and Health Management

With continuous further development of cable television technology, the integrating and intelligent degree of digital TV network increases constantly. And people are paying more and more attention to issues such as the stability, reliability, maintainability, security, detectability, life cycle cost, etc. which may provide favorable conditions for the generation and development of prognostic and health management (PHM) of the network equipment. In this paper, the background knowledge of the distributed digital TV signals detection system was illustrated simply at first, and then the basic concept, function and current situation of PHM were introduced. On that basis, the system design of PHM was reflected from system structure and functional component by aiming at realizing this project. In the end, the equipment health examination module has been designed specifically from the operating principle, module composition, function realizing process and decision-making support.

Fault Prognostic Based on Hybrid Method of State Judgment and Regression

Fault prognostic is one of the most important problems in equipment health management system. This paper presents a hybrid method of mixture of Gaussian hidden Markov model (MG-HMM) and fixed size least squares support vector regression (FS-LSSVR) for fault prognostic. The system is established based on three parts. The first part trains the MG-HMM and FS-LSSVR model. According to the known samples, several MG-HMM models can be learned based on expectation maximization (EM) algorithm. Then, the forward variables can be calculated based on these MG-HMM models. Based on these forward variables, the corresponding FS-LSSVR models are built. All the MG-HMM models and corresponding FS-LSSVR models are combined into a model library. The second part recognizes the unknown sample based on the model library. This part obtains the MG-HMM model and FS-LSSVR model by maximization likelihood calculation between the unknown sample and MG-HMM models. The third part of the system calculates the forward variables based on the MG-HMM obtained from the second part. These forward variables are inputted into the corresponding FS-LSSVR model to compute the remaining useful life (RUL) of the unknown sample. Finally, we carry out experiments on benchmark data set to verify the proposed method. The results illustrate the effectiveness of the hybrid method.

Integrated Instrumentation and Sensor Systems Functional Model and Taxonomy

This article presents a systems engineering functional architecture tailored to guide the development of integrated instrumentation/sensor systems (IISSs; incorporating communications media and interconnections and signal acquisition) for application to aerospace equipment in the following activities: test and evaluation, system diagnostics and health management (DHM), and advanced system control. The functional architecture suggested a taxonomy for IISSs based on defined variants of the functional architecture and morphological variations on the functional elements. The functional architecture and derived taxonomy focus attention on the sensor system interfaces and the transformations of the sensed parameters as the key to the definition and categorization of new configurations and implementation of integrated IISSs that promise to support the following: improved aerospace system verification and validation, more effective and responsive equipment health management, and a greater scope for precise control of system behavior and performance.

Optimization of dynamic sequential test strategy for equipment health management

Testing is the premise and foundation of realizing equipment health management (EHM). To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy (DSTS) for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision process (POSMDP) is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process (BSMDP) through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run expected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.

Integrated Instrumentation and Sensor Systems Enabling Condition-Based Maintenance of Aerospace Equipment

The objective of the work reported herein was to use a systems engineering approach to guide development of integrated instrumentation/sensor systems (IISS) incorporating communications, interconnections, and signal acquisition. These require enhanced suitability and effectiveness for diagnostics and health management of aerospace equipment governed by the principles of Condition-based maintenance (CBM). It is concluded that the systems engineering approach to IISS definition provided clear benefits in identifying overall system requirements and an architectural framework for categorizing and evaluating alternative architectures, relative to a bottom up focus on sensor technology blind to system level user needs. CBM IISS imperatives identified include factors such as tolerance of the bulk of aerospace equipment operational environments, low intrusiveness, rapid reconfiguration, and affordable life cycle costs. The functional features identified include interrogation of the variety of sensor types and interfaces common in aerospace equipment applications over multiplexed communication media with flexibility to allow rapid system reconfiguration to adapt to evolving sensor needs. This implies standardized interfaces at the sensor location (preferably to open standards), reduced wire/connector pin count in harnesses (or their elimination through use of wireless communications).

Research on Lifecycle-Oriented Health Management Philosophy and Key Technologies for Complex Equipment

Significant changes have taken place in service pattern for equipment health management and health management plays an important role in the lifecycle management of complex equipment. To resolve the existing problems in health management of complex equipment, this paper proposes a lifecycle-oriented health management philosophy, summarizes the connotation and basic characteristics of the philosophy. A three-dimensional model consisting of lifecycle level, health management knowledge level and application model level is constructed to realize the philosophy, which elaborates the components, developments and applications of equipment lifecycle health management. On that basis, key technologies for philosophy implementation are analyzed, which lays the foundation of system construction for lifecycle-oriented health management.

Data flow analysis of plant and equipment health and safety management

Purpose – Construction plant and equipment accident statistics suggest constant re‐evaluation of health and safety (H&S) systems is beneficial. This paper aims to process analyse plant and equipment H&S management systems on UK construction sites, with a view to applying information and communication technology (ICT) to them as an improvement mechanism.Design/methodology/approach – Five construction project case studies drawn from members of the former Major Contractors Group yield rich H&S process data. These are analysed using data flow diagram (DFD) techniques, to evaluate processes and proffer system improvements incorporating ICT.Findings – Causes of unsafe practice regarding management of construction plant and equipment are found to include: aspects of the plant itself, management processes and operator competence. A new ICT “process paradigm” is suggested, the architecture of which incorporates mobile computing, automatic identification and data collection and a management information system.Resea...

A novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM)

As a new maintenance method, CBM (condition based maintenance) is becoming more and more important for the health management of complicated and costly equipment. A prerequisite to widespread deployment of CBM technology and practice in industry is effective diagnostics and prognostics. Recently, a pattern recognition technique called HMM (hidden Markov model) was widely used in many fields. However, due to some unrealistic assumptions, diagnositic results from HMM were not so good, and it was difficult to use HMM directly for prognosis. By relaxing the unrealistic assumptions in HMM, this paper presents a novel approach to equipment health management based on auto-regressive hidden semi-Markov model (AR-HSMM). Compared with HMM, AR-HSMM has three advantages: 1) It allows explicitly modeling the time duration of the hidden states and therefore is capable of prognosis. 2) It can relax observations’ independence assumption by accommodating a link between consecutive observations. 3) It does not follow the unrealistic Markov chain’s memoryless assumption and therefore provides more powerful modeling and analysis capability for real problems. To facilitate the computation in the proposed AR-HSMM-based diagnostics and prognostics, new forward-backward variables are defined and a modified forward-backward algorithm is developed. The evaluation of the proposed methodology was carried out through a real world application case study: health diagnosis and prognosis of hydraulic pumps in Caterpillar Inc. The testing results show that the proposed new approach based on AR-HSMM is effective and can provide useful support for the decision-making in equipment health management.

HMMs for diagnostics and prognostics in machining processes

Despite considerable advances over the last two decades in sensing instrumentation and information technology infrastructure, monitoring and diagnostics technology has not yet found its place in health management of mainstream machinery and equipment. This is in spite of numerous studies reporting that the expected savings from widespread deployment of condition-based maintenance (CBM) technology would be in the tens of billions of dollars in many industrial sectors as well as in governmental agencies. It turns out that a prerequisite to widespread deployment of CBM technology and practice in industry is cost efficient and effective diagnostics and prognostics. This paper presents a novel method for employing hidden Markov models (HMMs) for carrying out both diagnostic as well as prognostic activities for metal cutting tools. The methods employ HMMs for modelling sensor signals emanating from the machine (or features thereof), and in turn, identify the health state of the cutting tool as well as facilitate estimation of remaining useful life. This paper also investigates some of the underlying issues of proper HMM design and training for the express purpose of effective diagnostics and prognostics. The proposed methods were validated on a physical test-bed, a vertical drilling machine. Experimental results are very promising.