Distributed Condition Monitoring Research Articles

A Hybrid, Distributed Condition Monitoring System using MEMS Microphones, Artificial Neural Networks, and Cloud Computing

A Hybrid, Distributed Condition Monitoring System using MEMS Microphones, Artificial Neural Networks, and Cloud Computing

AI-Based Self-Learning System in Distributed Structural Health Monitoring and Control

Artificial intelligence is predicted to play a big part in self-learning, industrial automation that will negotiate the bandwidth of structural health and control systems. The industrial structural health and control system based on discrete sensors possesses insufficient spatial coverage of sensing information, while the distributed condition monitoring has been mainly studied at the sensor level, relatively few studies have been conducted at the artificial intelligence level. This paper presents an innovative method for distributed structural health and control systems based on artificial intelligence. The structural condition was divided into regional and local features, the feature extraction and characterization are performed separately. Structural abnormality recognition and risk factor calculation method were proposed by considering the response values and the distribution patterns of both the regional and the local structural behaviours. The test results show that the method can effectively identify the full-scale and local damage of the structure, respectively. Subsequently, structural safety assessment method for long-span structures at kilometres level in view of fully length strain distributions measured by distributed fiber optic sensors were developed. A series of load tests on the long-span structure were carried out. Finite element (FE) model was developed using finite element code, ABAQUS, and an extensive parametric study was conduct to explore the effect of load cases on the structural responses. The differences in the structural response results among load test, structural safety assessment and FE simulation were investigated. It is shown that AI-based self-learning system could offer suitable speed in deployment, reliability in solution and flexibility to adjust in distributed structural health monitoring and control.

A hybrid tree sensor network for Condition Monitoring system to optimize maintenance policy

Wind energy is the leading candidate between the renewable energy sources as alternative to burning fossil fuels. A proper and accurate condition monitoring plan is mandatory to ensure high reliability and availability required by energy production systems. This paper proposes a Wireless Mesh Network to implement a widely distributed condition monitoring system for a wind farm. Using different types of sensor, the condition monitoring system evaluates the health state of each turbine. The aim of the work is to propose an architecture able to identify possible incipient failures in the most critical turbine’s components. Using this system, it is possible to guarantee continuity of service minimizing the unplanned maintenance operation due to hidden failure.

Condition monitoring of distributed systems using two-stage Bayesian inference data fusion

In industrial practice, condition monitoring is typically applied to critical machinery. A particular piece of machinery may have its own condition monitoring system that allows the health condition of said piece of equipment to be assessed independently of any connected assets. However, industrial machines are typically complex sets of components that continuously interact with one another. In some cases, dynamics resulting from the inception and development of a fault can propagate between individual components. For example, a fault in one component may lead to an increased vibration level in both the faulty component, as well as in connected healthy components. In such cases, a condition monitoring system focusing on a specific element in a connected set of components may either incorrectly indicate a fault, or conversely, a fault might be missed or masked due to the interaction of a piece of equipment with neighboring machines. In such cases, a more holistic condition monitoring approach that can not only account for such interactions, but utilize them to provide a more complete and definitive diagnostic picture of the health of the machinery is highly desirable. In this paper, a Two-Stage Bayesian Inference approach allowing data from separate condition monitoring systems to be combined is presented. Data from distributed condition monitoring systems are combined in two stages, the first data fusion occurring at a local, or component, level, and the second fusion combining data at a global level. Data obtained from an experimental rig consisting of an electric motor, two gearboxes, and a load, operating under a range of different fault conditions is used to illustrate the efficacy of the method at pinpointing the root cause of a problem. The obtained results suggest that the approach is adept at refining the diagnostic information obtained from each of the different machine components monitored, therefore improving the reliability of the health assessment of each individual element, as well as the entire piece of machinery.

Research and design of distributed fault diagnosis system in nuclear power plant

Nuclear power plant (NPP) is a complex system with abundant operation data and various fault types. Moreover, in most cases, change of system parameters and prompt of the alarm system can not necessarily tell us directly what types the fault belongs to or where it lies in. When multiple faults occur, there is no one-to-one relationship between the fault symptom and the fault itself. Furthermore, the degree and response rate of the fault are different, so some faults are gradual and some are sudden. It is thus clear that for different faults, we need to conduct combined diagnosis with a variety of diagnostic methods to ensure accuracy and instantaneity in NPP fault diagnosis. According to the characteristic of the distributed function of equipment and the digital instrument and control (I&C) system, we studied and designed the distributed condition monitoring and fault diagnosis system in NPP. Based on the “disassemble-synthesizing” diagnostic idea, this paper proposed an intelligent diagnosis method which applied the fuzzy neural network (FNN) in doing local diagnosis and multi-source information fusion technology in global diagnosis. The simulation result showed that this method can quickly and accurately complete the tasks of diagnosing different levels of the single fault and different types of multiple faults.

Validation of vibration measurements for heavy duty machinery diagnostics

Latest development of distributed condition monitoring for heavy duty machinery has revealed a major challenge concerning reliable data acquisition. Due to frequent failures of diagnostic algorithms caused by incorrectly registered data, validation of vibration signals became a very attractive field of research. The paper shows how classical data acquisition procedures might be enhanced by implementation of signal validation methods based on both machine process parameters signals and machine vibration signals. Presented validation covers analysis of process parameters, data stability constrains and also independent and comparative validation of vibration signals. Benefits of applied techniques are illustrated on real data from a wind turbine.

Distributed embedded condition monitoring systems based on OSA-CBM standard

This paper presents an approach to distributed condition monitoring systems that offers reusable software for a class of condition monitoring (CM) applications. The focus is on an open software framework for development of CM applications stemming from 1) OSA-CBM specification, which is a standard promoting interoperability, and 2) a component framework that enables reuse, configuration and rapid deployment. The framework is developed using Java and RMI middleware, and its application is demonstrated through a distributed gearbox CM system. The approach provides software enabled capability to distribute the CM data process across the hardware platforms to meet the given system configuration.

Distributed Data and Information Fusion for Nuclear Reactor Condition Monitoring

An increasing number of civil nuclear reactors world wide are approaching, and have in many cases exceeded, their original design lives. In order to satisfy regulatory requirements and ensure that the reactors are safe to operate, whilst maximizing availability and ensuring economic viability, the volume of installed condition monitoring has increased significantly in recent years. Due to the age of many stations however, there is often the lack of a modern, integrated IT infrastructure with many, often overlapping, data sources of varying measurement types and sample rates. This paper describes a method for the distributed condition monitoring of nuclear reactors using intelligent software agents, which by means of a common ontology can autonomously collect and analyze data from multiple locations. The use of data and information fusion to derive more detailed condition monitoring results is shown to be a task well suited to agent based condition monitoring and a prototype system designed along these principles is described. Case studies are presented of the application of the prototype system to condition monitoring analysis of real data sets from nuclear reactors in the UK.

Distributed monitoring and diagnosis system for hydraulic system of construction machinery

This paper mainly presents a distributed monitoring and diagnosis system for the hydraulic system of construction machinery based on the controller area net (CAN) field bus. The hardware of the distributed condition monitoring and fault diagnosis system is designed. Its structure including the sensors, distributed data acquisition units, central signal processing unit, and CAN field bus is introduced. The software is also programmed. The general software design and its realization are studied in detail. The experiments and applications indicate that the distributed condition monitoring and fault diagnosis system can effectively realize its function of real-time online condition monitoring and fault diagnosis for the hydraulic system of construction machinery.

Distributed flexible reconfigurable condition monitoring and diagnosis technology

As manufacturing becomes increasingly decentralized, flexible and reconfigurable, more research needs to be done on monitoring and diagnosis technology that accommodate these new trends. The distributed condition monitoring and diagnosis technology based on the “flexible and reconfigurable” concept is studied here. A condition monitoring diagnosis model based on the distributed flexible and reconfigurable idea is proposed in this paper. The component makeup and functions of this model are discussed in detail. The model can fulfill in most instances the manufacturing system requirements for changing the configuration of the monitoring diagnosis system according to different manufacturing system configurations. This model also realizes the flexibility and reconfigurability of the monitoring diagnosis system in some degree. The model has already spawned a successful prototype for monitoring a chemical plant in accomplishing monitoring and control of the production process and equipment. Finally, some future research work is pointed out.