Available online at www.sciencedirect.com
ScienceDirect Procedia Engineering 87 (2014) 1290 – 1293
EUROSENSORS 2014, the XXVIII edition of the conference series
WLAN-enabled sensor nodes for cloud-based machine condition monitoring P. Bellagente, C.M. De Dominicis, A. Depari, A. Flammini, S. Rinaldi, E. Sisinni*, A. Vezzoli Department of Information Engineering, University of Brescia, Via Branze 38, Brescia (25123), Italy
Abstract In the recent past, Machine Condition Monitoring (MCM) has gained popularity, in order to extend equipments’ lifetime and reduce unscheduled downtime. The original contribution of this work is the proposal of a WLAN-enabled sensor node capable to locally store prolonged acquisition sessions. Log chunks are periodically sent directly to the Internet, so that MCM-meaningful data can be analyzed by means of cloud services. Traditionally, MCM is based on long-term complex analysis of costly, time consuming and manually-executed measurements of vibrations, thermal profiles and other significant quantities. Both wireless sensor networks and cloud services have been already proposed to overcome these limitations. However, such an approach usually requires one or more gateways for sensor data to converge towards IP-based infrastructure. This network architecture is overcome by the proposed approach, as verified by means of a proof-of-concept prototype, highlighting pros and cons and effectiveness of the solution. © 2014 TheAuthors. Authors.Published Published Elsevier 2014 The byby Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the scientific committee of Eurosensors 2014. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the scientific committee of Eurosensors 2014 Keywords: Machine condition monitoring; cloud-based architecture
1. Introduction Traditionally, MCM data acquisition is performed by dedicated hardware collecting data from many sensors (e.g., accelerometer, IR camera…) and further data processing and fusion is implemented by a personal computer [1], [2]. It must be highlighted that, nowadays, the trend of manufacturing is moving toward regionalization; this implies that
* Corresponding author. Tel.: +39-030-371-5445; fax: +39-030-380014. E-mail address:
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1877-7058 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the scientific committee of Eurosensors 2014 doi:10.1016/j.proeng.2014.11.683
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machines used for production can be located everywhere around the world. Clearly, also maintenance is affected since the plant to be monitored can be far away from the office of the managers and fault diagnosis experts too. For this reason, since the days in which the Internet has become a reality, many works have been presented describing potentiality of using web-based services for remotely access machines’ fault diagnostic systems [3]. Nowadays, the ubiquitous availability of Internet connections allows complex algorithm execution to be moved to a cloud-based architecture. The gap between actual sensor output and the cloud is usually solved by means of dedicated gateways. This work tries to overcome the poor scalability of this approach suggesting and validating the adoption of the Internet-of-Things paradigm at the sensor level, as already proposed in other application fields. In fact, the relatively high power consumption of WLAN radios can be mitigated by low duty-cycle transmissions, i.e., locally logging readouts and sending data with long cycle time. 2. The proposed solution The proposed WLAN-enabled logger has been implemented exploiting a SanDisk Eye-Fi 4 GB SD card [4], which embeds a Wi-Fi interface (Fig. 1a). Originally designed to add wireless connectivity to cameras, the Eye-Fi automatically transfers snapshots to a remote server host via Simple Object Access Protocol (SOAP) on a HTTP connection. The card is connected via a SPI link to a microcontroller (μC) supervising the sensor signal conversion. Data to be logged have to be encapsulated into a container mimicking a JPEG image file; the μC also takes care of this task. In fact, data can be stored incrementally into the SD flash memory; in the end, when the image-like file is closed, the WLAN section awakens and tries to connect to an access point of a infrastructure mode configured network (Fig. 1b). Once the link is established, the Eye-Fi starts the authentication process and, if successful, the actual user data transfer can take place. On the server side, the received data can be suitably processed; for instance, the geotag service is available upon subscription, adding position-related information to the image, according to GPS format and exploiting neighboring WLAN networks information.
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b) Fig. 1. a) Simplified block diagram of the proposed WLAN-enabled sensor. b) General scheme of the overall cloud-based MCM.
3. Experimental results The experimental setup used to verify performance of the real-world prototype is sketched in Fig. 2. An Arduino UNO board based on a low-cost Atmel ATMega328 has been adopted. A glue-logic section has been added to buffer signals from the μC, operated with a 5V power supply, to the Eye-Fi card, operated with a 3.3V power supply. Supposing to deal with autonomous and battery supplied sensors, some tests about power consumption of the Eye-Fi section of the sensor node have been carried out. In particular, consumption measurements have been performed using a shunt resistor (1 ȍ), the voltage drop of which has been buffered by means of an instrumentation amplifier (INA111 from Texas Instruments, gain set to G = 10) and acquired with a DSO (Agilent MSO-X 3014A). Subsequently, to evaluate the effectiveness of the proposed approach, the arbitrary waveform generator embedded in the DSO has been used to create a synthetic waveform mimicking the (acceleration) sensor output. In particular, a sinusoidal, square and triangular waves having a period T § 10 ms have been generated. The analog-to-digital conversion is carried out by the converter embedded in the μC, configured to sample the input signal at 1.35 kSa/s and with a nominal vertical resolution of 10 bit.
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AWG
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Fig. 3a shows consumption measurements conducted when transferring a 16 kB file (the minimum size allowed); the three upper lines show signals for the SPI communication with the Eye-Fi, whereas the last one is related to the current absorption. Since in this phase we are interested only on power consumption, a purposely written firmware writing a test file in the flash memory hosted in the Eye-Fi has been developed and executed by the μC. In Fig. 3a, sections identified with 1 and 2 are related to the card awakening and the Wi-Fi scan after the reset, respectively; in section 3 (lasting about 2 s), the 16 kB file is actually written in the flash memory; section 4 represents the “radio-on” phase, which includes the initial network binding (about 14 s), the actual file transfer (about 16 ms, not distinguishable in the figure for zoom reasons), and a waiting phase (60 s in the figure, but configurable down to 30 s, as better detailed later on); finally, in section 5 the card is back to the idle state. A detail of the “radio-on phase” is shown in Fig. 3b, when transferring a 160 MB file. The average current consumption during binding and waiting phases is about 150 mA, whereas during the actual file transfer over Wi-Fi is 280 mA. Average current during the SD file writing (leftmost part of the plot in Fig. 3b or section 3 in Fig. 3a) is 25 mA whereas in the idle state (rightmost part of the plot in Fig. 3b or section 5 in Fig. 3a) is 7 mA. Actual Wi-Fi transfer time is about 140 s. Interesting to notice that the data transfer of Fig. 3a requires about 3 mAh, whereas data transfer of Fig. 3b, involving a 10000 times larger file, requires about 15 mAh, only 5 times more. This is because the actual transfer phase is preceded by the Wi-Fi binding operations and followed by the waiting phase, which have significant current consumption and fixed duration, independently on the file size. In the case of large file to transmit, as in Fig. 3b, energy consumption related to binding and wait phase are comparable with energy for the file writing and Wi-Fi transmission; however, if a small file has to be transferred, as in Fig. 3a, the energy overhead required by binding and waiting phases is predominant. This fact suggests that maximum energy efficiency can be obtained by limiting the transmission events and maximizing the size of the files to be transmitted.
current [A]
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Fig. 3. a) DSO screenshot of a power consumption test related to a 16 KB Wi-Fi file transfer. b) Detail of the power consumption test during the “radio-on” phase of a 160 MB Wi-Fi file transfer.
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It must be highlighted that the waiting phase is not strictly required by the adopted communication technology. In fact, this additional active period is forced by the Eye-Fi itself to make easier sending to the cloud a sequence of snapshots taken in (relatively) short amount of time. The Eye-Fi can be configured with different waiting time, no shorter than 30 s. The last experiment confirmed the capability of the proposed system to effectively log and transmit samples acquired by the μC. Fig. 4 shows the plots of the acquired waveforms (generated by the AWG, see Fig. 2) retrieved from the server and processed off-line by a remote application. As expected, we have 13/14 samples per period.
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Fig. 4. The generated waveforms acquired and elaborated by a remote server.
All the experiments confirmed that wireless transmission starts only after the completion of a JPEG-like file (i.e. characterized by the header 0xFFD8 and the trailer 0xFFD9), no matter the time required for transferring data from the μC to the embedded flash memory through the SPI link. 4. Conclusions This paper suggests the adoption of cloud computing paradigm for supporting wireless stations for MCM. Experiments with a real-world prototype, developed around the Eye-Fi, confirm that the proposed approach is an effective solution for logging and sending measurement readouts over a wireless link to a cloud service provider. The results obtained show that power consumption can be an issue for an autonomous battery powered solution; however, the overall lifetime can be sufficiently extended using a very low duty-cycle approach, e.g., transferring (large amount of) logged data only few times per day. References [1] G.N. Marichal, M. Artés, J.C García-Prada, An intelligent system for faulty-bearing detection based on vibration spectra, Journal of Vibration and Control, 17 (2011), 931–942. [2] K. Teotrakool, M.J. Devaney, L.A. Eren, Adjustable-Speed Drive Bearing-Fault Detection Via Wavelet Packet Decomposition, IEEE Transactions on Instrumentation and Measurement, 58 (2009), 2747–2754. [3] E. Sisinni, C. M. De Dominicis, A. Depari, A. Flammini, L. Fasanotti, M. Tomasini, Performance assessment of vibration sensing using smartdevices, Proceedings of IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May 12-15, 2014, pp. 1617-1622. [4] Guo Zhongwen, Chao Liu, Yuan Feng, Feng Hong, CCSA: A Cloud Computing Service Architecture for Sensor Networks, Proceedings of 2012 International Conference on Cloud and Service Computing (CSC), November 22-24, 2012, pp. 25-31. [5] Eye-Fi specifications, available online at www.eyefi.com
