Intelligent diagnosis and prognosis of industrial networked systems pdf

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intelligent diagnosis and prognosis of industrial networked systems pdf

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Intelligent Fault Diagnosis and Prognosis for Engineering Systems.pdf

The fault detection and diagnosis FDD along with condition monitoring CM and of rotating machinery RM have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications.

However, induction motor IM has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Artificial intelligence AI techniques have proven their significance in every field of digital technology.

Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent AI methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

Many industries have adopted several measures in their drive to optimize the reliability, availability, and safety to reduce the maintenance cost of modern industrial systems and applications, which are vital to process [ 1 , 2 ]. Thus, condition-based maintenance CBM has gained a significant role in an industrial world [ 3 , 4 ].

However, CBM is applied in order to achieve early maintenance decisions through CM collected data [ 5 ]. Moreover, condition monitoring CM and fault detection and diagnosis FDD of rotating machinery RM [ 6 , 7 ] have recently gained huge attention [ 8 , 9 ].

However, predictive health monitoring PHM methods are important to guarantee the required health state of the machinery [ 13 , 14 ]. Figure 1 shows the main components of a typical CBM [ 17 ]. CM methods are categorized into two groups, invasive and noninvasive methods. On the one hand, invasive CM is considered to be simple and basic technique. On the other hand, it is hard to implement. To overcome this challenge, noninvasive CM methods are highly used nowadays [ 18 ].

As key components of industrial systems and applications [ 19 — 21 ], rotating machinery, such as motor, gearbox, wind turbines, generator, and engine, is vital equipment in modern industrial applications [ 22 ]. These important machines have to run efficiently, accurately, and safely [ 23 ]. Due to the criticality and importance of this issue, several analysis and studies were published during the past years where many different approaches have been investigated to improve the CM and FDD for rotating machinery [ 24 , 25 ].

Conventionally, the traditional CM and FDD methods such as model and signal as well as data-based methods [ 26 — 29 ] need to extract the diagnosable information manually from the raw data [ 30 ]. Following that, pattern recognition models were developed using the features vector in the classification process [ 31 ]. This scenario requires much experience knowledge and complex feature extraction methods [ 32 , 33 ]. Induction motor IM [ 42 — 49 ] is vital in industrial processes and applications [ 50 , 51 ].

Moreover, IM is extensively used, for example, in mining machines, automotive applications, pumps, blowers, fans, chemical machines, lifts, compressors, vacuums, conveyors cranes, and engines [ 52 — 59 ].

Figure 2 summarizes applications of the IM. All parts of IM stator, bearing, bar, and rotor are affected by stress, aging, vibration, long operating time, continuously monitoring, and electrodynamic forces [ 60 — 62 ]. Thus, any failure of any part of IM may cause a serious breakdown of the machine, which increases the maintenance cost and leads to heavy losses [ 63 , 64 ]. Figure 3 shows IM faults and their percentage. Rolling bearings [ 66 ] were considered to be the main component of rotating machinery [ 67 ].

However, bearings are used in several mechanical and electrical applications, including IM, turbines, medical devices, cars and trucks, engines, automobile industry, and aerospace [ 68 ]. Importantly, any failure of this basic component can lead to a serious breakdown of rotating machines [ 69 ].

Rolling bearing faults could be categorized by two main factors, location of the fault and nature of the fault. For location category, five main faults occurred including, imbalance shaft faults, ball faults, inner race faults, outer race faults, and cage faults. For nature category, two main faults are considered, including cyclic faults and noncyclic faults [ 70 , 71 ]. CM and FDD of bearing element bearings of RM are widely used to follow up the operation condition of the machine [ 72 — 74 ].

As a result, any failure may cause a serious breakdown, which increases the maintenance cost and leads to heavy losses [ 77 ]. Moreover, several data and model-based techniques have been introduced including signal processing-based techniques [ 78 , 79 ], image processing based techniques [ 80 — 83 ], intelligent techniques [ 84 , 85 ], data fusion techniques [ 86 — 90 ], data mining techniques [ 91 — 96 ], and expert system techniques [ 97 — 99 ].

All those techniques have used specific analyses to develop the FDD methodology to arrive at efficient and accurate results [ , ]. As shown in Figure 4 , the analyses used in those studies include chemical analysis, electrical analysis, and mechanical analysis, in more details, temperature analysis [ — ], vibration analysis [ — ], noise analysis [ , ], radio-frequency RF analysis [ — ], infrared analysis [ — ], current and voltage analysis [ , ], electromagnetic field analysis [ — ], oil analysis [ , — ], pressure analysis [ — ], ultrasound analysis [ — ], and sound and acoustic emission analysis [ , ].

Figure 5 shows a general block diagram of a noninvasive FDD for rotating machinery. As an example, preprocessing stage includes data denoising and filtering. However, most electrical and mechanical signals are nonlinear and nonstationary signals. Thus, denoising techniques have been extensively studied nowadays.

Table 1 shows a comparison between various CM analysis techniques. The study also points out the advantages and drawbacks of each method. Finally, research challenges and possible future trends directions in this field are also presented in this article.

The rest of the paper has been organized as follows. Firstly, background and general introduction are discussed in Section 2. Finally, challenges and future trends are discussed in Section 4. Nowadays, the need for earlier detection of faults for IM is crucial. Therefore, in the feature extraction stage, ensemble empirical mode decomposition EEMD is implemented followed by intrinsic mode functions IMF decomposition. To add a new layer of improvement, five single classifiers based on the probabilistic committee machine PCM and Bayesian learning machine are trained and used in the classification stage.

Furthermore, 1 the single probabilistic classifiers, 2 the single probabilistic and Bayesian machines, 3 pairwise-coupled, and 4 two classifiers without pairwise-coupling strategy are used for further comparison of classification. As a result, the proposed probabilistic committee machine method showed the superiority of diagnosing faults. In [ ], an online feature condition monitoring approach based on unsupervised feature learning dictionary learning under different operational conditions using vibration and acoustic emission signals is introduced.

This work also presents dictionary distance and signal fidelity driven methods and techniques for anomaly detection are also described. Moreover, time-propagated characteristics are used along with sparse approximation of signals received from vibration and acoustic emissions. Importantly, the results of three case studies, i.

As a result, under normal variation condition, the learned features change slowly in comparison with high-speed variation when a fault appears. In [ ], an FDD system of IM designed on multiscale entropy and support vector machine SVM in combination with mutual information algorithm is proposed. The aim is to retrieve the required entropy feature; techniques like vibration signals, sample entropy, and multiscale entropy are applied.

Importantly, a support vector machine classifier is used for the entropy feature vector. Furthermore, classification results showed that these SVM based entropy techniques could effectively diagnose various motor faults i. Moreover, for a feature extraction stage, Hilbert transform HT and continuous wavelet transform CWT are applied as advance signal processing techniques to retrieve features and characteristics from radial vibration signals and to detect rotor, bearing, and stator faults.

Importantly, three classifiers are employed in this research: the neural network multilayer perceptron , neural network radial basis function , and support vector machines. As a result, in this study, the performance of SVM is found to be the best compared with NN classifiers, i.

Moreover, radial vibration and stator currents are used. Four motor conditions are extracted and classified, including healthy induction motor, misalignment, unbalanced rotor, and bearing fault.

Kernel-nonlinear SVM along with Gaussian radial basis function is employed. As a result, SVM bootstrap based technique with features data fusion has an ability of classifying multiple and single faults for different operating conditions of the IM with good accuracy Moreover, nine mechanical and electrical faults are detected and classified using a multiclass SVM algorithm.

In the feature extraction stage, time domain of vibration and current signals is used to seek statistical features. As a result, for the vibration signal and mechanical faults, the MSVM showed an ability of predicting all faults, but it could not predict current signals based on electrical faults. In [ ], an automatic FDD approach of IM uses deep learning techniques to combine the feature extraction process with the classification process. Moreover, deep belief networks DBN are modelled for vibration signals to retrieve key features.

Importantly, the proposed approach could detect the fault directly from frequency distribution without needing traditional feature extraction methods. Furthermore, to elevate the classification accuracy and reduce training error, the proposed approach could learn multiple layers of representation and model high-dimensional data. In [ ], an unsupervised feature learning sparse autoencoder-based deep neural network approach for induction motor faults classification is proposed.

Moreover, the proposed approach detected and classified multiple faults, three-rotor faults bowed, unbalanced, and rotor bars , defective bearing, and stator winding fault.

Features obtained from a sparse autoencoder are used to train a neural network classifier. To avoid complex sensor data problems, deep learning technique is recently used. In [ ], deep learning for infrared thermal IRT images is introduced to detect various machine conditions. Moreover, convolutional neural networks NNs are employed. The accuracy of this method is at least 6. Importantly, it can be used for online FDD and CM when the access is very difficult such as in offshore wind turbines.

The bearing is a critical component in IM. Thus, robust and intelligent CM and FDD methods are highly needed to enhance detection, diagnosis, monitoring, and prognosis capabilities.

Bearing faults are considered to be a majority of faults in IM [ — ]. Furthermore, the RBM showed the best in terms of interpretation and reduction.

In [ ], an adaptive method for the health monitoring of rotating bearings using the vibration signal is introduced. The proposed method applies the empirical mode decomposition—self-organizing map EMD—SOM to find a confidence value CV and to find the degradation of the fault. As a result, SOM based technique showed a high ability for online condition monitoring, especially for limited computing resources cases.

Bayesian network [ , ] is a probabilistic statistical model, which uses a directed acyclic graph DAG to seek conditional dependencies.

System health monitoring and prognostics — a review of current paradigms and practices

Du kanske gillar. Ladda ned. Spara som favorit. Laddas ned direkt. Skickas inom vardagar. In an era of intense competition where plant operating efficiencies must be maximized, downtime due to machinery failure has become more costly. To cut operating costs and increase revenues, industries have an urgent need to predict fault progression and remaining lifespan of industrial machines, processes, and systems.

Intelligent Diagnosis and Prognosis of Industrial Networked Systems

The topic of an Open Invited Track should fall within the scope of the congress and address specific, well defined issues. Open invited tracks have no upper limit on the number of papers but only those having gathered more than five accepted papers will be part of the final conference program. Contributions to open invited tracks can be either regular papers pages length or Extended abstracts short paper of pages length, they will not be published in IFAC PapersOnLine, they will only appear in the congress preprints.

Intelligent Diagnosis and Prognosis of Industrial Networked Systems proposes linear mathematical tool sets that can be applied to realistic engineering systems. The book offers an overview of the fundamentals of vectors, matrices, and linear systems theory required for intelligent diagnosis and prognosis of industrial networked systems. In this study, the research focuses on the development of a general framework of intelligent maintenance system, concerning 1 the performance assessment and prediction of industrial network, 2 the fault diagnosis of networked automation systems, and.

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below! Lurie and Paul J. Westervelt, Jessy W. Government works Printed in the United States of America on acid-free paper Version Date: International Standard Book Number: Hardback This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use.

Vachtsevanos, F. Lewis, M. Roemer A. Hess and B.

As is known to us bearing is one of the most important components used in modern engineering machinery. Once the bearing fails, it will lead to serious consequences such as equipment damage and great economic loss. Fault diagnosis and prognosis for bearing are very important, which can effectively prevent unexpected failures and assist engineering technicians to implement targeted equipment maintenance [ 1 , 2 , 3 , 4 ]. Fault diagnosis is used for identifying its symptom and fault conditions, and prognosis approach is generally employed to implement the remaining life prediction by existing information and knowledge. Before implementing fault diagnosis and prognosis approaches, it is key for us to effectively extract the fault features of bearing signals, which have direct effects on the diagnosis precision and prediction of bearing. Therefore, the selection for signal features of bearing can comprehensively and concretely reflect the information condition of bearing from different levels [ 5 , 6 ].

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