The ever-changing competitive environment in today’s industrial markets is forcing owners and operators to maintain their assets in prime operating condition. Traditional monitoring of selected equipment or processes is labour intensive with parameters being monitored one at a time to record their conditions, and analyse trends. In addition to consuming valuable man-hours and creating potentially dangerous situations, traditional monitoring results can be inaccurate, thus increasing downtime and maintenance costs.
Today, the IIoT provides a means of consistently capturing, communicating and recording real-time and historic data from networks of physical objects such as process equipment and vehicles with embedded sensors, software, and network connectivity. This enables the IIoT components to collect and exchange data and allows plants to detect equipment problems and process inefficiencies sooner, resulting in efficiency improvements and a reduction in costs.
Predictive maintenance goes beyond preventive
Routine inspections, system tests, lubrication, parts replacement and keeping records of equipment deterioration are all fundamental strategies for any preventive maintenance (PM) programme. Asset managers or maintenance staff establish set schedules for PM activities with the goal of reducing unplanned downtime. During PM, the systematic replacement of deteriorating components and the identification and correction of equipment issues will prevent equipment failure. However, by itself, PM does little to reduce costs for labour and spare parts as determining the ideal time for PM is imprecise and focuses on estimates in lieu of the actual equipment condition. Oftentimes, perfectly good components are periodically replaced on a need-it-or-not basis. Furthermore, some PM activities can cause collateral damage due to human error and this only adds to downtime.
To minimise unplanned downtime beyond that achieved using PM, and further reduce parts and labour costs, condition monitoring is the cornerstone for evolving predictive maintenance (PdM) solutions. Condition monitoring to track asset/system pressure, temperature and humidity levels allows asset managers and maintenance staff to perform maintenance only when necessary. By providing real-time and historic data trends of assets and processes, the condition monitoring solution allows operators to detect and diagnose issues before they become problems. Also, using smart sensor hardware and analytic software, alerts can be delivered to operators when needed. PdM with condition monitoring allows optimisation of systems and assets based on actual data rather than reacting to unexpected events.
Particularly on process-critical assets and systems, integrating a PdM strategy into an existing PM program can substantially save on parts and labour, and reduce troubleshooting time through fast and precise diagnoses that maximise asset life spans.
Analytics through Bluetooth and cloud connectivity
Wireless smart sensors coupled with a software interface that enables users to visualise data collected from the sensors provides a condition monitoring and predictive maintenance solution rooted in the IIoT. Wireless sensors avoid the cost and complexity of a wiring infrastructure, and can be easily removed for modification as is typical during expansions. Monitoring a plant’s assets for temperature, pressure, humidity, dew point, flow and current usage plays a vital role in diagnosing inconsistencies, allowing users to predict and prevent downtime.
SensoNODE Blue and SensoNODE Gold wireless sensors are small in size and easy to install. They are well-suited for robust use in harsh environments, as they are constructed using 17-4 stainless steel wetted parts (or brass with humidity sensors) in a polycarbonate housing with fluorocarbon or nitrile body seals. SensoNODE Blue sensors utilise a Bluetooth radio module that lets users connect directly to a mobile device. They are ideal for quick troubleshooting of systems as well as assisting individuals that monitor the condition of assets in a route-based scenario.
Using Bluetooth technology, SensoNODE Blue sensors transmit data to the mobile software platform installed on a user’s mobile device. This allows simple, wireless monitoring of pressure, temperature, and humidity within a 250 m range of the sensors.
SensoNODE sensors have two operating modes: Connect and Beacon. Through the mobile app, users can view, manage and record data while in range. The Connect mode is used to establish a private one-to-one session with the sensor to manage the settings. The Beacon mode allows multiple users to view data from the sensors. In both modes, users can view measurements and visualize data with multiple tools. The direct link between the sensors and the mobile app puts vital information and analytics in the palm of the hand, enabling users to optimize asset performance.
SensoNODE Gold sensors are also wireless-based nodes that work on a 900 MHz frequency band. Users have the option of pressure, temperature, current, flow, humidity, dew point and a soon-to-be-released vibration node. These sensors interface with a gateway located on the premises that collects and buffers data. The gateway enables secure connections, manages the operating status, and is the data conduit to the cloud platform, as well as pushing commands from the cloud to the sensors. The cloud platform is accessed through a secure login Internet portal. It enables users to manage sensors, set thresholds and alarms, and visualises the data gathered from their assets through easy-to-use dashboards. Also, the cloud delivers email or text alerts to users when sensor levels fall above or below user-defined thresholds. This allows detection by users anywhere in the world of unexpected condition changes before they become problems.
Enabling predictive maintenance solutions in multiple industries
SensoNODE sensors and software present new opportunities for IIoT solutions in any industry where monitoring of rotary machinery and continuously run manufacturing equipment is needed, especially in applications that pose safety hazards to the user. Some type of rotary machinery is used in almost every manufacturing plant, and the moving parts can make the monitoring of these assets potentially dangerous. Such assets typically require a full shutdown for maintenance or monitoring. Uptime of continuous manufacturing processes is critical to avoiding costs associated with unplanned maintenance and lost revenue. Any single issue can halt production, causing delays in getting end products to customers. SensoNODE sensors and software monitor continuous processes and alert users when action is required to avoid potential issues and to keep operations running. As a predictive maintenance tool, they not only help to increase throughput, but also maintain high quality standards. Industries such as metal and aluminium foundries, steel plants, power generation, pulp and paper, material handling, and injection moulding present just a few of many potential applications.
Saving downtime and maintenance costs in power generation
Minimising maintenance costs and downtime is especially critical in the power generation market, given pressures to remain competitive with respect to dispatch. Assets must be well-maintained to maximise availability. Prime examples of process-critical assets in power generation are gas and steam turbines. Monitoring of multiple state conditions of asset levels allows operators to detect and diagnose problems or damage to turbines before they become issues.
Temperature
Turbines that are not well lubricated or cooled with clean oil are subject to overheating. Monitoring for drastic changes of a system’s temperature can help operators identify when filters and/or oil in the turbine may need to be replaced. Such changes can take place in a plant’s hydraulic lift oil pump, hydraulic power unit, or diverter camper controls.
Pressure
Changes in a system’s pressure can also indicate turbine issues and can happen within several sections of a plant, including hydraulic lift oil pumps, hydraulic power units, hydraulic cylinders, diverter damper controls and nitrogen generators. Increased fuel consumption and/or reduced output could be tell-tale signs of a more serious problem, such as compromised integrity of rotary components within the turbine and structural damage. Such issues can lead to displacement or damage to toothed gears, blade damage or fatigue failures, and other structural damage that will ultimately impact a system’s performance.
Humidity
Power generation equipment can operate in some very harsh conditions, including inclement weather, high winds, and constant motion. Increased humidity levels translate into excessive moisture, posing threats to a turbine’s gearbox, leading to corrosion, reduced efficiency, and ultimately breakdown. SensoNODE humidity sensors are ideally suited for monitoring ambient relative humidity over the full 0-100% range.
Humidity and pressure monitoring is also critical for a plant’s nitrogen generators to ensure that a ‘dry layup’ condition for heat recovery steam generators can be maintained. During layup of heat recovery steam generators, nitrogen from a plant’s nitrogen generator is applied to prevent the onset of corrosion in the boiler tubes. Blanketing the tubes that have been exposed to moisture with nitrogen displaces oxygen and prevents tube corrosion.
Conclusion
Accurate diagnostics are vital to maintain process-critical assets through initial commissioning, operation and refurbishment. Condition monitoring and predictive maintenance solutions rooted in the IIoT allow power plants to coordinate downtime rather than reacting to unscheduled outages, improving efficiency and lowering maintenance costs. Wireless sensors allow plants to avoid putting workers into dangerous situations and locations while preserving plant machinery.
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