Machine Condition Monitoring System Research Articles

Threshold lines identification for non-Gaussian distributed diagnostic features

Machine condition monitoring systems are frequently used in the industry, especially for critical infrastructure. Decision making is still challenging due to the lack of limit values (called also thresholds). Identification of thresholds is in particular difficult for unique machines with specific Health Index (HI) data properties. Our contribution is a procedure for threshold values identification for HI data with time-varying characteristics and non-Gaussian behaviour. The proposed methodology consists of few crucial steps, such as data segmentation, data modelling, and Monte Carlo simulations for quantile lines identification based on the fitted models. The proposed methods are based on robust statistics dedicated to non-Gaussian distributed data. Novelty of the paper is related to the extension of simple ”Trend+Noise” model for HI data and model-based framework for threshold lines estimation. Thus, the method provides more accurate results for non-Gaussian, time-varying data. We demonstrate the superiority of the proposed procedure over the classical ones. The presented simulation study clearly indicates the efficiency of the approach for non-Gaussian HI data. The methodology is applied to two real datasets with different properties (level of non-Gaussianity and presence of interdependence in the random part). We believe that proposed procedure could be considered as a guideline for future research and may be implemented in commercial monitoring systems.

Laser Based Vibration Sensor Through Mobile

Machine condition monitoring has gained momentum over the years and becoming an essential component in the today’s industrial units. A cost-effective machine condition monitoring system is need of the hour for predictive maintenance. The paper presents the design and implementation using vibration sensor, and also this system operated through smartphones. Vibration analysis plays a major role in detecting machine defects and developing flaws before the equipment fails and potentially damages. The concept of this project was to detect faulty equipment in industry machine so that before damaging the whole machine faulty equipment can be replace and improve the durability of machine.

Whitening CNN-Based Rotor System Fault Diagnosis Model Features

Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models.

Scopes, challenges and approaches of energy harvesting for wireless sensor nodes in machine condition monitoring systems: A review

Machine condition monitoring (MCM) plays an important role in reducing failure losses in the industry. Appropriate wireless sensor nodes (WSNs) used in MCM will allow the user to check the live status of machines. WSNs have numerous features, however, there are a lot of challenges in deploying the WSNs. One of the main challenges is the periodic replacement of batteries. Recently promising energy harvesting technologies have attracted the attention of researchers because of their potential to make the WSNs autonomous. This paper outlines the scopes of energy harvesters, potential ambient energy sources, and different energy harvesting techniques along with their advantages and disadvantages. In addition, various energy harvesting approaches established for WSNs have been reviewed and compared to enable easy selection of the best possible approach for MCM system. To provide intuition into future research prospects, the challenges faced by energy harvesting systems are summarized.

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault-diagnostic systems. The occurrence of failures in a machine will typically lead to nonlinear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this article, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault-diagnostic systems are summarized. Furthermore, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful toward future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault-diagnostic systems.

Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery

The application of cutting-edge technologies such as AI, smart sensors, and IoT in factories is revolutionizing the manufacturing industry. This emerging trend, so called smart manufacturing, is a collection of various technologies that support decision-making in real-time in the presence of changing conditions in manufacturing activities; this may advance manufacturing competitiveness and sustainability. As a factory becomes highly automated, physical asset management comes to be a critical part of an operational life-cycle. Maintenance is one area where the collection of technologies may be applied to enhance operational reliability using a machine condition monitoring system. Data-driven models have been extensively applied to machine condition data to build a fault detection system. Most existing studies on fault detection were developed under a fixed set of operating conditions and tested with data obtained from that set of conditions. Therefore, variability in a model’s performance from data obtained from different operating settings is not well reported. There have been limited studies considering changing operational conditions in a data-driven model. For practical applications, a model must identify a targeted fault under variable operational conditions. With this in mind, the goal of this paper is to study invariance of model to changing speed via a deep learning method, which can detect a mechanical imbalance, i.e., targeted fault, under varying speed settings. To study the speed invariance, experimental data obtained from a motor test-bed are processed, and time-series data and time–frequency data are applied to long short-term memory and convolutional neural network, respectively, to evaluate their performance.

Significance of Incorporating Chebfunction Coefficients for Improved Machine Fault Diagnosis

For any industry the efficiency and performance of rotating machinery/mechanical systems is a major concern. Bearings and gears are two essential parts in a rotating machinery and any defects in these components can lead to a major breakdown of the system thus causing large economical loss for the company. An appropriate machine condition monitoring system is essential in such scenarios for identifying the health of the machines. Therefore in this paper fault diagnosis of rotating mechanical systems is performed as a feature dependent-pattern classification problem. The machine is made to run in different good as well faulty conditions and the vibration signals are collected. Then chebfunction coefficients are extracted from the vibration signals as part of the feature extraction process. Finally, the extracted features are classified using regularized least squares (RLS) for identifying the good and faulty bearing as well as gear conditions of the machine. The evaluations are performed using different kernel functions and the average accuracy reported is 98% for bearing and gear data. The various experiments performed claims that the proposed system can be used for real-time fault diagnosis in rotating mechanical systems with sufficient accuracy.

Acoustic Emission Source Localization in Ring Gears from Wind Turbine Planetary Gearboxes

Condition monitoring and fault diagnosis are methods to achieve a higher reliability for complex mechanical systems. Due to the ability of acoustic emission (AE) technology to detect damages during at an early stage, AE has high potential to be considered in novel machine condition monitoring systems. Since AE waves are generated due to rapid releases of strain energy, such as crack propagation, roughness contact and mechanical impacts, it is possible to use multiple AE sensors to track down the AE source location. This paper presents an accurate and easy-to-use methodology to locate AE sources in ring gears from planetary gearboxes. The methodology uses AE wave’s time of arrival calculated from three signal differential envelopes. The time of arrival is subsequently used to analytically determine the angular position of the AE source. The proposed methodology was tested in two different real-sized wind turbine gearboxes using Hsu-Nielsen sources. The results showed that, with the correct selection of filter cut-off frequencies, the localization could be carried out with an error equivalent to only two teeth from the correct position, indicating the good performance of the approach.

Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review.

Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made.

Case Study: Vibration trip and post-event Analysis with Auto-Associative Neural Networks on a Large Steam Turbine

This 300 MW steam turbine at a coal-based thermal power plant is equipped with a protection system, a condition monitoring analysis software and an automatic diagnostic tool. The Machine Protection System (MPS) and Condition Monitoring System (CMS) configuration combines sensors, electronic hardware, firmware and software specific to this application. The protection system initiated a trip having identified high vibration. The trip prevented further damage. Subsequent analysis of the data using the condition monitoring software established the bearings most affected and pin pointed the source of high vibration. The data is post processed using an Auto-Associative Neural Networks (AANN) that has been trained with healthy data recorded several hours prior to the trip. AANN are methodologies widely used for novelty and anomaly detection. The AANN results indicates that such approach would be capable of detecting the failure event in advance compared to the automatic diagnostic system based on rules, demonstrating the validity of the approach in this context. Various aspects related to vibration: protection, condition monitoring, analysis, automatic diagnostics using rules and Neural Networks are presented and their results discussed.

Closed-loop design evolution of engineering system using condition monitoring through internet of things and cloud computing

Flexibility of a manufacturing system is quite important and advantageous in modern industry, which function in a competitive environment where market diversity and the need for customized product are growing. Key machinery in a manufacturing system should be reliable, flexible, intelligent, less complex, and cost effective. To achieve these goals, the design methodologies for engineering systems should be revisited and improved. In particular, continuous or on-demand design improvements have to be incorporated rapidly and effectively in order to address new design requirements or resolve potential weaknesses of the original design. Design of an engineering system, which is typically a multi-domain system, can become complicated due to its complex structure and possible dynamic coupling between domains. An integrated and concurrent approach should be considered in the design process, in particular in the conceptual and detailed design phases. In the context of multi-domain design, attention has been given recently to such subjects as multi-criteria decision making, multi-domain modeling, evolutionary computing, and genetic programing. More recently, machine condition monitoring has been considered for integration into a scheme of design evolution even though many challenges exist for this to become a reality such as lack of systematic approaches and the existence of technical barriers in massive condition data acquisition, transmission, storage and mining. Recently, the internet of things (IoT) and cloud computing (CC) are being developed quickly and they offer new opportunities for evolutionary design for such tasks as data acquisition, storage and processing. In this paper, a framework for the closed-loop design evolution of engineering systems is proposed in order to achieve continuous design improvement for an engineering system through the use of a machine condition monitoring system assisted by IoT and CC. New design requirements or the detection of design weaknesses of an existing engineering system can be addressed through the proposed framework. A design knowledge base that is constructed by integrating design expertise from domain experts, on-line process information from condition monitoring and other design information from various sources is proposed to realize and supervise the design process so as to achieve increased efficiency, design speed, and effectiveness. The framework developed in this paper is illustrated by using a case study of design evolution of an industrial manufacturing system.

Quality-Based Multiple-Sensor Fusion in an Industrial Wireless Sensor Network for MCM

The early alert monitoring system for an effective scheduled maintenance strategy based on a wireless technology requires reliable transfer of the diagnostic information between the sensor and the gateway. This paper presents an industrial wireless sensor network (IWSN)-based machine condition monitoring (MCM) system capable of overcoming a false indication caused by temporary loss of data, signal interference, or invalid data. We use multisensor fusion driven by a quality parameter, which is produced by each sensor node according to the data history outliers and the actual state of the node. The fusion node provides a quality evaluation on its output as well. This novel approach enables the propagation of information about the uncertainty of a measured value from the source node to the sink node. Thus, potential degradation of acquired or transferred diagnostic information is minimized. Instead of raw data, the signal features are transferred, so that bandwidth savings are rapidly improved. The proposed concept was experimentally verified on real wireless sensor network (WSN) hardware. The performance evaluated using the signal-to-noise ratio and false-alarm rate detection demonstrates the effectiveness of the proposed approach.

A Distributed Fault Detection System Based on IWSN for Machine Condition Monitoring

This paper introduces a novel framework for industrial wireless sensor networks (IWSNs) used for machine condition monitoring (MCM). Our approach enables the use of state-of-the-art computationally intensive classifiers in computationally weak sensor network nodes. The key idea is to split data acquisition, classifier building and training, and the operation phase, between different units. Computationally demanding processing is carried out in the central unit, while other tasks are distributed to the sensor nodes using over-the-air programming. The system is autonomously trained on the healthy state of a machine and then monitors a change in behavior which indicates a faulty state. Thanks to one-class classification, there is no need to introduce the faulty state of the machine in the training phase. We extend the diagnostic capability of the system using dynamic changes in the data acquisition and classification parts of the program in the sensor nodes. This enables the system to react to ambiguous machine states by temporarily changing the diagnostic focus. Compressing the information in the individual sensor nodes provided by in-node classification allows us to transmit only the classification result, instead of full signal waveforms. This enables the MCM system to be deployed with a large number of nodes, even with high sampling rates. The proposed concept was evaluated in IRIS IWSN by means of a rotary machine simulator.

A new machine condition monitoring method based on likelihood change of a stochastic model

In industry, a machine condition monitoring system has become more important with ever-increasing requirements on productivity and cost saving. Although researches have been very active, many currently available intelligent monitoring methods have common drawbacks, which are the requirement of defect model for every interested defect type and inaccurate diagnostic performance. To overcome those drawbacks, authors propose a new machine condition monitoring method based on likelihood change of a stochastic model using only normal operation data. Hidden Markov model (HMM) has been selected as a stochastic model based on its accurate and robust diagnostic performance. By observing the likelihood change of a pre-trained normal HMM on incoming data in unknown condition, defect can be precisely detected from sudden drop of likelihood value. Therefore, though the types of defect cannot be identified, defects can be precisely detected with only normal model. Defect models can also be used when defect data are available. And in this case, not only the precise detection of defect but also the correct identification of defect type is possible. In this paper, the proposed monitoring method based on likelihood change of normal continuous HMM have been successfully applied to monitoring of the machine condition and weld condition, proving its great potential with accurate and robust diagnostic performance results.

Methodology and Framework of a Cloud-Based Prognostics and Health Management System for Manufacturing Industry

Prognostics and Health Management (PHM) as a research discipline has been extensively studied in recent years, with many sophisticated techniques and intelligent algorithms developed for machinery data analysis, health assessment and decision making. PHM solutions for typical components such as roller bearing and machine tool have grown and proved to be reliable enough for industrial application. However utilization of PHM solutions in industry is still much limited due to high research, development and implementation costs. Such limitations are more severe in smaller scale factories and workshops where noample resource is available for implementing PHM systems. Efforts have been made to tackle such issue, for instance, Watchdog Agent® Toolbox developed by the IMS Center enables faster deployment of PHM solutions through algorithm modularization and standardization. With advantages that can be deliveredwith the emerging cloud computing paradigm, a cloud-based prognostics and health management system for manufacturing industry has been developed based on Watchdog Agent® tools and the ideology of PHM as a Service. In addition to traditional data acquisition and management functions in a machine conditionmonitoring system, the cloud based PHM platform is able to further provide on-demand, customizable and low-cost data analysis service. Machinery data accumulated within the cloud system further enables more advanced services such as machine-to-machine comparison, data mining and knowledge discovery.

Low-Cost Condition Monitoring System of Rotating Machinery Based on LabVIEW

In order to change the current status that machine condition monitoring system is only generally applied to key equipments of large-scaled and high-end business, a low-cost mini condition monitoring system of rotating machinery based on LabVIEW is proposed and designed in this paper. The system is not only of advantages of lower cost, stronger expandability and higher applicability, but also changes the condition that current systems emphasize too much on the comprehensiveness, universality and complexity. It is capable of meeting the wide range of condition monitoring of common rotating machinery, for faults diagnosis and predictive maintenance needs better, then, its potential application can be foreseen.

Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification

Nowadays, most factories rely on machines to help boost up their production and process. Therefore, an effective machine condition monitoring system plays an important role in these factories to ensure that their production and process are running smoothly all the time. In this paper, a new and effective machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed. Two classification characteristics, namely peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output ( p-value) are applied in this proposed machine condition monitoring system. Large PSR and p-value showed a good match among correlation of the input thermal image with a particular reference image, but reversely for small PSR and p-value match. In the simulation, log-polar mapping is found to have solved the rotation and scaling invariant problems in quaternion based thermal image correlation. Besides, log-polar mapping can possess two fold data compression capability. Log-polar mapping helps smoothen up the output correlation plane, hence making better measurement for PSR and p-values. The simulation results have also proven that the proposed system is an efficient machine condition monitoring system with an accuracy of more than 94%.

EXPERIMENTAL STUDY AND LOGISTIC REGRESSION MODELING FOR MACHINE CONDITION MONITORING THROUGH MICROCONTROLLER-BASED DATA ACQUISITION SYSTEM

Machine condition monitoring plays an important role in machining performance. A machine condition monitoring system will provide significant economic benefits when applied to machine tools and machining processes. By applying Taguchi design method, real-time pilot experimental study was conducted on a CNC machining center for monitoring the end mill cutting operations through the vibration data collection via a microcontroller-based data acquisition system. Featured machining signals were identified through data analyses and regression models were established that incorporates different combinations of featured machining signals and machining parameters in using logistic regression modeling approach. The onsite tests show that the developed logistic models including the featured machining signals can correctly distinguish worn and new cutting tools. Therefore, they can help construct decision-making mechanism for machine condition monitoring.

OCEAN TURBINES — A RELIABILITY ASSESSMENT

This paper identifies factors that impact reliability and safety of ocean turbines. We describe how physical and environmental factors will impact the design of its machine condition monitoring (MCM) system. Environmental factors like fouling, corrosion, and inaccessibility of equipment sets this MCM problem apart from those encountered by wind turbines, hydroelectric plants, or even ship hulls and propellers. Fouling constitutes the primary and most persistent source of failure. In addition to compromising turbine efficiency and reliability, fouling reduces sensor data quality — masking faults that will ultimately lead to failure. Unmitigated fouling triggers a form of biological succession known as flocculation that may eventually attract threatened species of tortoises and cetaceans to this rotating machinery. We review and suggest refinements to a class of non-toxic biologically-inspired anti-fouling techniques known as engineered topographies. Advances in this area will enable turbines to operate in portions of the water column that maximize momentum flux while minimizing retrieval cost.

Remote Machine Condition Monitoring Using Wireless Web Technology

A remote real-time machine condition monitoring system is reported in this paper, which is applied for diagnosis and prognosis of gearboxes’ working condition. Within the system, the diagnostic classification is performed by pattern recognition using statistic parameters, and remote diagnostic capability is enhanced by applying Wireless Web technology. An online signal-processing scheme is adopted based on time-frequency analysis, digital filtering and statistic parameter algorithm to detect early fault signals of gears and to provide expert advice for decision making for maintenance. The effectiveness of the developed remote diagnostic system is verified via experimental investigation of monitoring a gearbox on a test rig under different conditions.