Yokogawa has developed a compact, intelligent, low-power battery-operated LoRaWAN wireless sensor, that can be deployed to perform vibrational condition monitoring in the field. Affectionally known as the Sushi Sensor, it can be installed on a plant in large numbers to acquire sensory data. This is centrally collected and analysed by AI algorithms running in the background, whose function is to predict the imminent failure of rotating machines. This paper introduces both the technology and the effectiveness of deploying these small technological wonders in the field.
Background
Condition monitoring (CM) is an approach that utilises sensory data in the field to analyse the performance and efficiency of machinery while in operation. Through the combination of real-time data that is meticulously analysed by advanced software packages, based primarily on artificial intelligent algorithms, the system can identify patterns within the captured data.
Anomalies in the historical trends of the performance of a specific machine can be the indication of early stage deterioration, while simultaneously and objectively interpreting the data to predict the remaining life of a machine while in operation. When an anomaly is detected, an alert is sent through to flag the early detection so that corrective preventative action can be taken by the engineering team before the anomaly leads to a major breakdown or catastrophe. This approach offers a holistic, plant-wide view of a plant’s health and safety while dramatically increasing a machine’s productivity, operating life and business profitability.
In contrast to standard plant operation, production and safety control systems, condition monitoring is based on hundreds to thousands of sensory data points in the field that are used to capture the vast array of data monitoring variables that need to be analysed. Compact wireless sensors like the Sushi Sensor are an attractive solution in such an application. They have the benefits of reduced wiring costs, ease of installation, rapid integration, and wireless network connectivity.
Vibration monitoring
Integrated into the condition monitoring diagnostic tool package, vibration monitoring is one of the most effective means of detecting and preventing the early stages of equipment failure by monitoring key aspects of rotating machines such as imbalance, misalignment, looseness, and bearing wear.
The integrated sensor converts the mechanical oscillation of the object relative to a static point into an output signal that constitutes the sequential sample data points that need to be captured and analysed relative to a timestamp of events. As each component of a rotating machine generates its own fundamental frequency, a cumulative complex output waveform signature is ultimately generated. These signatures are unique to the machines and form the baseline from which potential failures are identified, to the point that analysis can even isolate the fault on a component level, for example excessive wear on a specific bearing. Rotating machines of various sizes like motors, pumps, gearboxes, compressors and fans can be easily and effectively monitored by a Sushi Sensor mounted on the outer casing.
LoRaWAN technology
Long Range Wide Area Network (LoRaWAN) technology is designed to connect wireless IoT devices in the field. It is characterised by a long range and low data rate (0,3 to 50 Kbps) at a very low power consumption, with a frequency that ranges from 433 to 915 MHz, country dependent. Deployed in a star topology configuration in which localised gateways relay messages between end nodes and the network server, each gateway acts as a transparent bridge converting RF packets to IP packets and vice versa.
LoRa is defined as the physical layer responsible for the wireless modulation radio transmission (RF) technology, while LoRaWAN is the wide area networking protocol that is built on top of LoRa that wirelessly manages the bidirectional communication securely. This is governed, maintained and standardised by the LoRa Alliance governing body.
This radio modulation technique transmits data packets utilising a chirp spread spectrum (CSS) in which a chirp consists essentially of a sinusoidal waveform whose pulse frequency either increases (up-chirp) or decreases (down-chirp) over a specified time. Furthermore, the Spread Spectrum Factor (SF) defines the duration of the chirp within a specific frequency band or bandwidth (BW).
The network components termed nodes within the LoRaWAN system architecture are divided into three classes:
• Class A devices are low power consumption nodes like sensors, in which data can only be received at specific window times after data is transmitted.
• Class B devices are high power consumption nodes like actuators, which have periodically synchronised receiving window times when data can be received.
• Class C devices are mains-powered nodes like gateways, where the receiving window remains open except during transmission.
The Sushi Sensor is designated as a Class A LoRaWAN classified node, powered by a SAFT LS lithium-thionyl battery to ensure long service life. Operating at normal environmental temperatures, a typical battery life of four years can be expected.
NFC technology
Near Field Communication is a short range RF wireless technology that enables two NFC-compliant devices to exchange data securely, quickly and easily with a single touch. The Sushi Sensor is equipped with an NFC-enabled interface, which enables users to interact with the device in terms of monitoring and configuration using an Android mobile phone and the Sushi Sensor App available from the Google playstore.
The Sushi Sensor app also forms an integral part of the cryptographic protection process, determining how each individual sensor is paired to the host gateway to ensure optimum security. Furthermore, the Sushi Sensor extracts and stores the GPS coordinates from the commissioning phone to tag its relative position in the field. A smart alternative tracking solution like an onboard GPS chip would have shortened the battery life and increased the overall cost.
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| Email: | yma.info-za@yokogawa.com |
| www: | www.yokogawa.com/za |
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