Therefore, boundary conditions can be formed for the under-examination parameters. Eng. By the virtue of the diversity of installed sensors, crosschecking the measured values, is enabled and therefore more accurate predictive models can be trained. The above-mentioned functionalities are based on the adaptation of the methodology presented in the research work of Mourtzis et al. Based on the spectrograms of the faulty and the healthy datasets, features can be extracted and classified for future use via the use of a Support Vector Machine (SVM). Trans. Data are used to optimize the internal parameters and make the models work properly. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud. (2018). Considering the broad diffusion of IoT and AI sensors, especially in smart manufacturing, predictive maintenance allows the maximization of productivity and product quality while also reducing costs by scheduling preventive maintenance tasks. Remaining useful life prediction of induction motors using nonlinear degradation of health index. The Set Up New Line functionality is targeted for new customers, or customers that acquired new equipment, i.e. In this section the architecture of the framework for the DAQ device will be discussed. Then, with the use of spectrograms, useful features were extracted and based on these features, with the use of a Support Vector Machine, the faults could be classified. Further to that the contribution of this research work extends to the presentation of a custom Data Acquisition (DAQ) device and a framework for processing the data via the Digital Twin of the equipment for the calculation of Remaining Useful Life of critical components. It is stressed out that the OEM has already integrated sensing systems on the majority of their products for monitoring purposes. 0000004405 00000 n
Simon is an experienced sales leader with a technical background. A classification of the most common DL frameworks is as follows: Neural Networks (NN) (Chryssolouris, 2006; Chen et al., 2019), Deep Neural Networks (DNN) (Zhao et al., 2017), Convolutional Neural Networks (CNN) (Li et al., 2018; Mourtzis et al., 2020a), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) (Zhao et al., 2017), Gated Recurrent Units (GRU) (Chen et al., 2019), Recurrent Neural Network (CNN-RNN) (Banerjee et al., 2019), Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) (Kong et al., 2019), Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) (Lei et al., 2018). doi:10.1016/j.procir.2020.03.051, Lei, Y., Li, N., Guo, L., Li, N., Yan, T., and Lin, J. For the development of the Graphical User Interfaces (GUI), a Universal Windows Platform (UWP) (Microsoft, 2018) application has been developed. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. It is worth noting that they achieved an increase in terms of accuracy of approximately 10 percent. Renew. An also well-known subset of the above-mentioned concept is Machine Learning (ML). 3 becomes: Based on the pose estimation steps described in the previous paragraph, for the registration of the AR content using Android-based platforms, a fiducial image target is required. Consequently, for the user pose estimation in the case of Microsoft HoloLens, the developed application initially prompts the user to select/setup an initial point of reference. It is stressed out that the model consists of several subsystems, or else functional blocks, in an attempt to increase the resolution of the simulation model, such as the compressor, the evaporator, the condenser, and the refrigerator compartment. 6:578379. doi: 10.3389/fmech.2020.578379. 0000003175 00000 n
Condition monitoring technologies are usually designed for larger plants rather than for workshops with a few machine tools. Predictive maintenance helps anticipate when maintenance should be performed on machinery. An AR module is provisioned in order to facilitate the monitoring process of the industrial equipment. Moreover, for the setup of the WSN the X-CTU (X-CTU, 2020) application from Digi has been utilized. Its also possible to integrate predictive maintenance with BIM to further optimize the process. Preventive care, efforts to prevent a disease before needing to treat it, isnt a new idea and originated in Ancient Greek. Artif. Recently, Capgemini defined an interesting architecture that allows predictive maintenance AI models to be run on edge devices directly. Ahmed, M., and Khan, A.-S.P. To better understand how predictive maintenance works, lets see an example of its application. From manufacturing to agriculture, theres no industry thats not affected by the ongoing process of automation. In this context, from a technical point of view, predictive maintenance can be seen as a set of artificial intelligence techniques that make use of neural networks, deep learning and other machine learning solutions. London: Palgrave. 62(4), 821832. Placing the operator at the centre of Industry 4.0 design: modelling and assessing human activities within cyber-physical systems. Thus, AI in predictive maintenance helps companies save money and resources by tailoring maintenance routines to each piece of equipments needs, rather than forcing them to a rigid schedule. doi:10.1016/j.jms y.2018.01.006, Unity, . For example, we can all recognize a damaged exhaust or an unusual sounding engine. Additionally, Deep Learning (DL) techniques have been applied for the integration of systems in edge computing, setting edge nodes in edge services and terminal devices, using DL architectures for predictive analysis with quick preprocessing and accurate performance classification to assess the life expectancy of components. Most research studies on intelligent prognosis and health management (PHM) analysis using data-driven approaches by deducing correlations between data from different sensors ( e.g. Remaining useful life prediction based on health index similarity, Reliab. This allows a better subdivision of the connectivity bandwidth and a smoother transition in terms of the costs required to convert all the devices to 5G. doi:10.1016/j.promfg.2017.07.257, Microsoft, . 185, 502510. Eng. The two have the same purpose of making the most out of a machines features with proper functioning as long as possible, but follow different methodologies. PdM can be defined as a series of processes, where data is collected over time in order to monitor the state of equipment, in a manufacturing system. doi:10.1016/j.ress.2019.02.002, Loutas, T. H., Roulias, D., and Georgoulas, G. (2013). Therefore, the profit becomes a problem with two possible solutions, either the minimization of operating costs, or the maximization of income. By continuously monitoring machinery and using a predictive machine learning model on real-time data, it becomes possible to anticipate extraordinary events and avoid their negative impact on production. doi:10.1016/j.procir.2020.04.130, Mourtzis, D., Siatras, V., Angelopoulos, J., and Panopoulos, N. (2020c). These two subsystems often are not located in the same room/building, thus require different DAQ devices to be installed. In addition to that, the simulation results are also combined/fused with the data gathered from the physical machine so as to predict the RUL of specific components of the equipment. (A) Settings for a new refrigerator line; (B) Settings for new/old sensors. (2019). They are real examples of how companies are coping with the lack of skilled labor on the market. Available at: https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/global-perspectives-ai-adoption.html (Accessed June 20, 2020), Kong, ., Cui, Y., Xia, Z., and He, L. (2019). Further to that, this functionality enables both the OEM and the client to communicate via a video call session, where the OEM can visualize the field of view of the user and with the use of basic 3D tool representation, the client can perform maintenance tasks in real time. doi:10.1016/j.ymssp.2017.11.016, Li, X., Zhang, W., and Ding, Q. 0000019340 00000 n
As a result with the proposed methodology, it is possible to predict future asset malfunctions based on the simulation of the refrigeration cycle and plan accordingly their production schedule so that the equipment downtime is further minimized. 0000001411 00000 n
All rights reserved. Three ways to estimate remaining useful life for predictive maintenance. Clean. 0000005771 00000 n
The second solution is for monitoring the condition of the asset while inspecting it physically. (2020), both maintenance technicians and experts are keen on integrating AR and MR solutions in their line of job, in order to achieve better communication and most importantly to limit the complexity of the maintenance procedures. Therefore for the calculation of xc a 3 3 rotation matrix is utilized, denoted by R, as per the Eq. Swiss Federal Institute of Technology Lausanne, Switzerland, Indian Institute of Technology Dhanbad, India. In addition to that, since the customer, could get an estimation of the upcoming failure, they are able to schedule a maintenance session with the OEM much faster and fitted to both ends schedule without creating great disturbances. When implementing predictive maintenance technology, it does not matter how big the company is. As far as the sampling rate is concerned, the DAQ device collects feedback from the installed sensors on a varying rate. This brings great financial benefit to the producer. The main benefit of the proposed methodology, is that it can provide time estimations about future equipment malfunctions, which by extension can enable both the OEM and the client to act proactively and in time, in order to further minimize the equipment downtime.
Cloud-based augmented reality remote maintenance through shop-floor monitoring: a product-service system approach. Predictive Maintenance. As the name indicates, Remaining Useful Life, also referred to as RUL, describes a wide variety of algorithms which aim to predict the remaining life of assets and/or their components, ultimately developed under a predictive maintenance framework. Last update April 12, 2021 by Vito Gentile. Procedia CIRP. Finally, in Concluding Remarks and Outlook, the paper is concluded, and future research points are discussed by the authors. Today, companies usually rely on cloud services, such as Microsoft Azure AI. Eng. Syst. Robot. Inquire about NVIDIA Deep Learning Institute services. This trend is posing a great challenge for engineers, as such developments will enable the creation of robust systems that can monitor the current status of the machines and by extension to predict unforeseeable situations. Among the latest developments of the current industrial revolution, advances in high-end digital technologies are entailed, including Extended Reality (XR). This work has been partially supported by the H2020 EC funded project SPARE Sensor-based Product monitoring system to support Augmented Reality remote maintenancE (GA No: 80765 5-0020271). Procedia Manufacturing. Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Mech. Depending on the systems of predictive maintenance in use, they can be notifications on a visual display, alerts sent to a mobile app, or another method of feedback to take the system managers attention. The training process is usually done on highly performant machines, with one or more GPUs capable of significantly speeding up the heavy training process. 182, 208218. Ultimately, the goal of this experiment series is to generate fault datasets, i.e. Sci. Augmented reality in support of intelligent manufacturing a systematic literature review.
However, since the data are available on the server, it is of great importance to create an application for monitoring the current situation of the machines. Furthermore, Smart Computers will be capable of examining all possible scenarios and suggest viable solutions in a fraction of time compared to humans. doi:10.1016/j.ress.2017.11.021, Liu, Y., Hu, X., and Zhang, W. (2019). Procedia Manufacturing. The development of the DAQ device is based on the design of a custom circuit board in combination with an Arduino micro-controller which incorporates all the required modules for the data acquisition from the sensors attached to the board, the pre-processing of the data, an interface for user interaction and a wireless network module for the data transmission to the Cloud Database. Previously, the manufacturer would run the crusher for a predetermined amount of time. This means not only saving wear and tear on the crushing equipment but more importantly, saving time and increasing the volume of gravel delivered per shift. 0000001594 00000 n
For the setup of the WSN, XBee modules are utilized.
Consider the case of a robotic arm used within a production chain in industry and imagine that this arm is programmed to move an object from one position to another. For the simulation, the fluid properties of the R134a refrigerant were also imported in the model. Like any other machine learning model, the models used for predictive maintenance need a training process. We can imagine this with the example of a gravel crusher. Train a classification model using GPU-accelerated XGBoost and CPU-only XGBoost. It should be clear by now that implementing effective predictive maintenance can significantly impact productivity and reduce costs in companies that use machinery within their production chain.
Further to that in two recent systematic literature reviews, presented by Palmarini et al.
Moreover, the value of products will eventually be focused on their software parts not on their specification or implementation functions under the Product Service Systems (PSS) paradigm (Mourtzis et al., 2018). Data is then processed locally from machine learning predictive models, which can reactively detect anomalies, benefitting from being as close as possible to the source. The benefits of using UWP is the multi-platform implementation, the ease of configurability, ease of implementing security protocols, serviceability of the framework and updates distribution. 0000017906 00000 n
Each of these patterns represents a classification of the possible situations of the under-examination machine, or cluster of machines. For the purposes of the experiments, a scenario of compressor malfunction has been examined. In parallel, any anomalies that arose were resolved as soon as possible and considered extraordinary events. More specifically, for the development of the main functionalities of the application, the code scripts are written in C# programming language. 0000013541 00000 n
More specifically, an XBee module (Figure 3B) is installed on each of the DAQ devices and another one is installed on a computer which acts as the network coordinator. With the advancement of Information and Communication Technology (ICT) and cutting-edge technologies such as Mixed-Reality (MR), Augmented Reality (AR) and Virtual Reality (VR), the academic domain is expanding this strategy by leveraging the advantages of AR for data visualization during maintenance operations (Mourtzis et al., 2017; Mourtzis et al., 2018; Palmarini et al., 2018). 139(6), 061011. doi:10.1115/1.4035721, Mourtzis, D., Vlachou, E., Milas, N., and Xanthopoulos, N. (2015). Ind.
More specifically, the development team assisted by the editor can create virtually any configuration of systems and functionalities, so that the framework can be adapted to the actual needs of the corresponding company.
Simulation in the design and operation of manufacturing systems: state of the art and new trends. Based on this setup the monitoring and simulation runs were executed at the premises of the case study provider. 0000016666 00000 n
He got into entrepreneurship in 2016 when he founded a company focused on predictive machine maintenance using sound analysis. Eng. Res. s\Yra'N:YO@?ll=(J_W4=(AP@e}?e'NC'F~o7
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Zr'G2}9yo)q~_2 93, 977982. In its essence, XR is an umbrella term, often used by engineers and researchers around the world, in order to describe technologies such as Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR) (Mourtzis et al., 2020b). With the increasingly wide spread of artificial intelligence and machine learning, the adoption of predictive maintenance can be seen as one of the most prominent examples of such data-driven solutions.
By extension, the prediction of malfunctions will enable companies to schedule their production more efficiently, whilst it makes them more adaptive to any disturbances caused within the company limits. Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth. Pavel is a tech visionary, speaker, and founder of AI and IoT startup Neuron Soundware. Fundamentals of Deep Learning for Computer Vision. Therefore, in this paper, the modelling, design and development of a Predictive Maintenance and Remote Monitoring system are proposed, based on the utilization of Artificial Intelligence algorithms for data acquisition, fusion, and post-processing. 58, 121134. However, thanks to Artificial Intelligence (AI) and the Internet of Things (IoT) solutions, a new maintenance method emboldens this goal even further and makes it a lot easier to achieve.
Although the development and the implementation of the proposed framework have yielded promising results, there are several implications that must be addressed before such solutions reach an acceptable maturity level and by extension, become commercially available. J. Manuf. Afterwards, in order to handle the data arrived at the WSN coordinator, the corresponding COM port is listened by the PC via a Python script and the data are uploaded to the database and saved within the corresponding CSV file. However, in order to enable the communication between the DAQ device (Fig. 104, 799834. The more machines, the more opportunities for the neural network to learn and apply detection of unwanted sounds. However, as a core topic in prognostics and health management, the remaining useful life (RUL) prediction based on monitoring data ca be used to prevent a failure triggered (Lei et al., 2018). Overall maintenance costs are reduced by 5 to 10% and maintenance planning time is even reduced by 20 to 50%! 83 37
doi:10.1007/s10845-016-1228-8, Vorraber, W., Gasser, J., Webb, H., Neubacher, D., and Url, P. (2020). FIGURE 1. doi:10.1016/j.ress.2017.02.007, Keywords: artificial intelligence, predictive maintenance, remote monitoring, augmented reality, machine learning, Citation: Mourtzis D, Angelopoulos J and Panopoulos N (2020) Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality. The most important implication faced, is that the calculation of the RUL cannot be performed in real-time thus inducing a certain amount of latency in the AR visualizations. Pract. In Figure 4, the developed model within the Simulink environment is presented. *Correspondence: Dimitris Mourtzis, mourtzis@lms.mech.upatras.gr, Data-Driven Cognitive Manufacturing - Applications in Predictive Maintenance and Zero Defect Manufacturing, View all
It is stressed out that although the development of a UWP application enables multi-platform support, the AR functionalities are only available for handheld devices, such as Android-based mobile phones and tablets, and Head Mounted Displays (HMD) such as Microsoft HoloLens.
Mech. 11, 12961302. 0000004294 00000 n
In order to further notify the customer about an upcoming maintenance action or if any piece of equipment requires special attention, certain alerts have been implemented as presented in Figures 6 and 7. A conveyor delivers different sized pieces of stone into grinders, which are to yield a given granularity of gravel. Video: Is It Possible to Go From YAML to TypeScript in Cloud Automation? It is estimated that following the implementation of the proposed framework in similar equipment, i.e. Data-driven maintenance: combining predictive maintenance and mixed reality-supported remote assistance. 0000000016 00000 n
Secondly, DELETE requests are not allowed for anyone trying to connect to the Cloud Platform. Identifying early defects of wind turbine based on SCADA data and dynamical network marker. The remainder of the paper is structured as follows. Before we delve further into how AI can help machinery to work efficiently, its important to not confuse between the routines of preventive vs. predictive maintenance. The WSN follows the star topology, meaning that the one XBee is connected to a PC and acts as the WSN coordinator. Eng. A framework for automatic generation of augmented reality maintenance & Repair instructions based on convolutional neural networks, 53rd CIRP conference on manufacturing systems (CMS 2020). Applying this idea would allow the system to react more quickly to anomalies, with no latency due to possible network issues (which might be significant in some situations). doi:10.1016/j.ress.2019.01.006, Chryssolouris, G. (2006). In the Industry 4.0 environment, maintenance should do much more than simply prevent the downtime of individual assets. 205, 107241. doi:10.1016/j.ress.2020.107241, Wolfartsberger, J., Zenisek, J., and Wild, N. (2020). Syst. The idea behind this is to utilize similar datasets in order to improve the predicting accuracy of the Digital Twin. Based on that send the send the corresponding alert with precise maintenance instructions. The time for inspection was calculated to be approximately 5h. In the following figure, the time estimations for the current situation as well as the corresponding times with the adoption of the proposed methodology are presented Figure 8. A novel deep learning approach for machinery prognostics based on time Windows. Globally the AI adoption is surging at enormous rates, as it becomes apparent in the report presented in (Hupfer, 2019), from where it can be concluded that AI adoption marked a surprising 270% increase in a timespan of 4 years along with an increase in global spending of around 80 billion dollars Figure 1.
Available at: https://www.arduino.cc/ (Accessed October 2,2020), Banerjee, I., Ling, Y., Chen, C. M., Hasan, A. S., Langlotz, P. C., Moradzadeh, N., et al. Intell. Procedia CIRP. Such a critical scenario is not possible if the maintenance technology is equipped with artificial intelligence in addition to the mechanical knowledge of the machines. This way, technicians or facility executives can perform the necessary updates or repairs to maintain the machines health and well-being while ensuring safety and efficiency in their vicinity.
Measurement. In the following paragraphs the proposed system architecture will be discussed in detail. The key aspects of the proposed methodology are the DAQ device, which conforms to the latest IoT standards. 0000008457 00000 n
It is difficult (if not impossible) to anticipate (and thus avoid) the occurrence of extraordinary maintenance events; Scheduled maintenance has a constant cost, which is unavoidable even when the machinery is performing in normal fashion. 0000007067 00000 n
Procedia CIRP. Procedia Manufacturing. Recent studies also show that unplanned downtime is costly, with an estimation of $50 billion per year for global producers (Deloitte, 2017a). Machine Learning Types & algorithms (Adapted from Ahmed and Khan, 2019). Such a service allows defining a digital replica of the monitored machine directly on the Cloud in more detail. 88, 139144. If they are not, a new training process is started. MD conceived the idea and supervised the writing and the experimentation of the research work, AJ is responsible for the conceptualization of the project and the writing of the paper, PN conducted the research for similar works and contributed in the results as well as in the writing of the paper. (2019). Nevertheless, it is often too late to drive the car safely home from a holiday. 
However, as hardware and data transmission and processing get progressively cheaper, the technology is getting there too. He can stop the crushing process at the right point. More specifically, the PCB supports wired connectivity, through 3.5mm jack ports for the sensors. 102, 104546. doi:10.1016/j.conengprac.2020.104546, Zhao, ., Liang, B., Wang, X., and Lu, W. (2017). For the implementation of the developed AR-based application, an Android-based tablet and a Microsoft HoloLens HMD are used. This model may in turn anticipate when a specific maintenance intervention needs to take place, by observing the performance of the robotic arm in real-time along with temperature data. Further to that, in Figure 5A the physical form of the image target, whereas in Figure 5B, the features recognized by the device are overlaid on the image target. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). Manufacturers of mechanical equipment such as lifts, escalators, and mobile equipment use this today, for example. Moreover, the whole inference process can work without connectivity, allowing operators to activate the appropriate maintenance procedure in anomaly detection. instructions how to enable JavaScript in your web browser. User tracking and pose estimation for the Android-based devices is based on the recognition of a feature-rich image target, as in the one presented in Figure 5. Eng. In this step, two spectrograms are created, one for the fault data and one for the healthy data. Consequently, the use of 5G is crucial in this context to support reliable and effective streaming of data to the servers. q=lZ+HEq.C7I
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doi:10.3390/app10051855, Mourtzis, D., Vlachou, A., and Zogopoulos, V. (2017). Received: 30 June 2020; Accepted: 30 October 2020;Published: 17 December 2020. Reliability Engineering & System Safety. From the practical implementation of the developed framework in the industrial partner, it became evident that the refrigerator downtimes can be reduced by approximately 20%, since both the clients and the OEM were capable to monitor the status of their equipment and by extension, with the use of the AI algorithm, the RUL prediction for crucial components of the refrigerator system, the client got a trustworthy estimation of when their equipment should be maintained. In the future, the Digital Twin will also be improved. That is why many manufacturing companies are looking for solutions that automate and reduce maintenance costs. As explained in the previous section, this approach also allows for operation in the absence of connectivity, which is a major plus in contexts where network availability might not be taken for granted. Indeed, the benefits of PdM such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic. In the future, these maintenance routines can unleash tremendous savings in time and resources, while also reducing the downtime of systems or the risk of injury. doi:10.1016/j.isatra.2020.08.031, Wen, P., Zhao, S., Chen, S., and Li, Y. In Proposed System Architecture, the proposed system architecture is presented. In an attempt to make the proposed framework more general, a custom editor has been developed for supporting the functionalities of the framework itself. 0000001036 00000 n
Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. The modification involved the creation of additional subsystems which are used for the simulation of faults.