Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Iqbal, Rahat; Maniak, Tomasz; Doctor, Faiyaz; Karyotis, Charalampos

doi:10.1109/tii.2019.2902274

Cited by 164 publications

(50 citation statements)

References 27 publications

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“…In a different setting, fault detection for computer-based assembly lines of the automotive industry was studied in Reference [59]. This process is usually performed through boundary checking while the analysis of complex non-linear signals is manually performed by experienced personnel.…”

Section: Deep Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

222

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Deep Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

222

View full text Add to dashboard Cite

show abstract

“…However, Hadi et al [5] used an RNN model-based fault diagnosis technique with a time step of 1 to detect faults without such data. Rahat et al [7] used a deep autoencoder [15] and Artificial Neural Network (ANN) to provide various data for fault detection in the industry, and its performance was twice as high as the conventional rule-based method. Lee et al [16] detected more than 95% of the faults in an Air Handling Unit (AHU) using five hidden layers with 200 neurons.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, studies have been actively attempting to integrate artificial intelligence technology with such systems to improve the intelligence and utilization of sensors. In particular, research is underway to reduce energy by forecasting the energy demand and supply [6], designing automatic fault detection technology using deep learning methods [5,7], and developing smart sensors with artificial intelligence (AI) [8]. Because the data used to train the AI come from the sensors, they are essential for enhancing the intelligence of buildings.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Air Flow Control in an HVAC System through Deep Learning

Son

Kim

2019

Applied Sciences

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Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN).

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“…In the finance domain, machine learning is often applied in the credit risk prediction, 11 investment behavior estimation, 12 and financial products pricing 13 . In the assembly domain, machine learning is an emerging technology for the quality diagnosis, 14 fault detection, 15 and automated learning from demonstrations 16 . According to the above reviews, it can be concluded that machine learning is a hotspot to address short‐term prediction or classification tasks in engineering, but the real‐time and dynamic changing environment in engineering puts forward higher requirements for the decision‐making ability of the system or robot.…”

Section: Introductionmentioning

confidence: 99%

A deep transfer‐learning‐based dynamic reinforcement learning for intelligent tightening system

et al. 2020

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Reinforcement learning (RL) has been widely applied in the static environment with standard reward functions. For intelligent tightening tasks, it is a challenge to transform expert knowledge into a recognizable mathematical expression for RL agents. Changing assembly standards make the model repeat learning updated knowledge with a high time-cost. In addition, as the difficulty and low accuracy of designing reward functions, the RL model itself also limits its application in the complex and dynamic engineering environment. To solve the above problems, a deep transfer-learningbased dynamic reinforcement learning (DRL-DTL) is presented and applied in the intelligent tightening system. Specifically, a deep convolution transferlearning model (DCTL) is presented to build a mathematical mapping between agents of the model and subjective knowledge, which endows agents to learn from human knowledge efficiently. Then, a dynamic expert library is established to improve the adaptability of algorithm to the changing environment. And an inverse RL based on prior knowledge is presented to acquire reward functions. Experiments are conducted on a tightening assembly system and the results show that the tightening robot with the proposed model can inspect quality problems during the tightening process

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Fault Detection and Isolation in Industrial Processes Using Deep Learning Approaches

Cited by 164 publications

References 27 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Sensorless Air Flow Control in an HVAC System through Deep Learning

A deep transfer‐learning‐based dynamic reinforcement learning for intelligent tightening system

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