Modeling and fault diagnosis design for HVAC systems using recurrent neural networks

Shahnazari, Hadi; Mhaskar, Prashant; House, John M.; Salsbury, Timothy I.

doi:10.1016/j.compchemeng.2019.04.011

Cited by 101 publications

(38 citation statements)

References 19 publications

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“…Conventional methods of modeling a facility and detecting faults operate on the basis of historical fault data. 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.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, researchers have found that energy savings can be obtained by interlocking the sensor in the building with the control device [2][3][4]. Furthermore, HVAC system faults can be diagnosed by analyzing the sensor data so that the energy waste and failure of the device can be prevented [5]. In recent years, studies have been actively attempting to integrate artificial intelligence technology with such systems to improve the intelligence and utilization of sensors.…”

Section: Introductionmentioning

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%

See 2 more Smart Citations

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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sensorless Air Flow Control in an HVAC System through Deep Learning

Son

Kim

2019

Applied Sciences

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“…Today, companies and their production systems face challenging issues including a complexity derived from a high variation of products [8,9]. These high-complexity/low-volume environments driven by Industry 4.0 trend and techniques represent a very difficult and challenging situation for production companies [12][13][14][15]. In today's practice, small and medium-sized enterprises (SME) can apply smart factory production line solution embedded with IoT and CPS technology to embrace the opportunity of small lot sizes, different product lines with low budgets for automation investments [8,12,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Developing a Quick Response Product Configuration System under Industry 4.0 Based on Customer Requirement Modelling and Optimization Method

et al. 2019

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In the Industry 4.0 environment, the new manufacturing transformation of mass customization for high-complexity and low-volume production is moving forward. Based on cyber-physical system (CPS) and Internet of things (IoT) technology, the flexible transformation of the manufacturing process to suit diverse customer manufacturing requirements is very possible, with the potential to provide digital “make-to-order” (MTO) services with a quick response time. To achieve this potential, a product configuration system, which translates the voice of customers to technical specifications, is needed. The purpose of this study is to propose a methodology for developing a quick-response product configuration system to enhance the communication between the customer and the manufacturer. The aim is to find an approach to receive requests from customers as inputs and generate a product configuration as outputs that maximizes customer satisfaction. In this approach, engineering characteristics (ECs) are defined, and selection pools are initially constructed. Then, quality function deployment (QFD) is modified and integrated with the Kano model to qualitatively and quantitatively analyze the relationship between customer requirements (CRs) and customer satisfaction (CS). Next, a mathematical programming model is applied to maximize the overall customer satisfaction level and recommend an optimal product configuration. Finally, sensitivity analysis is conducted to suggest revisions for customers and determine the final customized product specification. A case study and an OrderAssistant system are implemented to demonstrate the procedure and effectiveness of the proposed quick response product configuration system.

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“…In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts are made for the application of intelligent algorithms [66,67] including new approaches to fault detection and isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69][70][71][72][73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77]. In addition, a technician needs knowledge and a good amount of experience to correctly use such sensors [78][79][80][81][82][83][84].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Modeling and fault diagnosis design for HVAC systems using recurrent neural networks

Cited by 101 publications

References 19 publications

Sensorless Air Flow Control in an HVAC System through Deep Learning

Sensorless Air Flow Control in an HVAC System through Deep Learning

Developing a Quick Response Product Configuration System under Industry 4.0 Based on Customer Requirement Modelling and Optimization Method

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

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