Effectively maintaining complex industrial processes is crucial for sustaining productivity and reducing costs. Condition monitoring (CBM) is an alternative to scheduled maintenance programs, using data from sensors in the Industrial Internet of Things (IIoT) to monitor trends in performance degradation before failures occur. This article will explore this topic and recommend suitable power modules for sensor nodes.
Developing countries are moving towards high levels of industrial automation to achieve high output with high flexibility and low cost. Similarly, data centers, distribution warehouses, and infrastructure aim to operate "lights out" (unmanned factories) with minimal human intervention to reduce labor costs. "Industry 4.0," or the Industrial Internet of Things (IIoT), is an integral part of this, pushing intelligence to the "edge" of processes, closer to where monitoring and control are needed to accelerate response times. Therefore, a communication network is required between monitoring and sensors and central control. This could be achieved through the "cloud," by collecting and analyzing data, and then incorporating that data into control algorithms to optimize processes in so-called smart factories.
Smart factories offer numerous benefits, enabling low-cost products and services while significantly reducing energy consumption. But what happens when problems arise? We're not talking about the "rise of machines" here, but rather a simple mechanical failure, a processor malfunction due to cosmic ray interference, or a "random failure" caused by countless other factors. With most of the world's power infrastructure exceeding 25 years of service life, the ever-increasing failure rate is a cause for concern. Therefore, system designers provide redundancy for critical components to handle single or double failures, selecting high-margin components for reliable operation depending on the application. However, "wear and tear" is a reality for mechanical parts and even electronics; capacitors dry out and age, surge limiters fail, and semiconductors develop lattice defects over time.
Continuous maintenance is required.
We can wait for a failure to occur and then address the problem at its source, adopting a "don't fix it if it's not broken" attitude. However, failures rarely occur only when it's convenient. This kind of "corrective" maintenance is an attractive approach for non-critical functions, such as a lighting fixture with a blown LED, or, as in our previous example of a multi-redundant system, where the system is not affected by a single failure. Of course, this assumes you are aware that a failure has occurred, making monitoring crucial. Sufficient spare parts inventory and manpower must be ensured at all times, and you must be on standby "just in case."
Most processes rely on regular "preventative" maintenance and inspections to maintain productivity. Simple mechanical systems require regular filter and oil changes, bearing inspections, and clearance adjustments. In electronics, this might involve replacing properly functioning but worn-out fuses, surge arresters, and electrolytic capacitors. Deciding when to act is not easy; acting too late can lead to catastrophic failures, while replacing parts too early may result in discarding good parts and wasting unnecessary work and costs. Therefore, scheduling is a challenge, sometimes based on usage time, usage patterns, or simply intuition and experience. High-availability systems typically undergo Failure Mode, Effects, and Criticality Analysis (FMECA) to scientifically predict the frequency and impact of failures. This minimizes parts inventory and schedules maintenance work at appropriate times.
State-based maintenance is the ideal approach.
When dealing with large and complex processes, it's difficult to accurately plan preventative maintenance. Therefore, condition-based maintenance (CBM) is an alternative. The ideal approach is to replace or adjust components based on their measured remaining service life. This involves understanding the current condition of a part—even if it's still performing well, it has begun to enter the "wear" stage. For example, an oil filter might have sufficient lubricating oil flow today, but with a 10% reduction in filtration efficiency. However, if you know that a 50% reduction is unacceptable and it will take another 10 weeks to reach that state, you can schedule its replacement in 8 weeks. Similarly, real-time analysis of changes in the vibration characteristics of a motor or machine can predict when a bearing will fail.
Performance changes and trends can be detected using parameters such as liquid level changes, vibration characteristics, infrared thermal imaging for non-contact temperature measurement, oil turbidity, current and voltage characteristics of power supply equipment, ultrasonic leak detection, and ozone sensors for arcing and corona discharge. While the cost of adding this level of intensive monitoring to an existing system might seem prohibitive in the short term, the long-term savings are significant. Fortunately, the scheduling and optimization of big data implementation for the Industrial Internet of Things (IIoT) also yields data for CBM analysis. Any sensor-specific functionality required for CBM is relatively easy to connect to processor nodes at the edge of existing processes. CBM data is inherently slow-changing, and the increased requirements for IIoT computing and communication are negligible, whether wired or wireless. The different maintenance regimes are illustrated in Figure 1.
CBMs need to provide a reliable power supply for the sensors.
IIoT nodes may already have the power required for remote sensors and data interfaces, potentially sourced from wired DC power, onboard batteries, local energy harvesters, or AC/DC converters. The environments in which sensors, IIoT nodes, and power supplies operate can be harsh and variable, with high power surges occurring whenever heavy machinery starts or stops, necessitating isolation between DC and AC power supplies. Furthermore, monitoring functionality must continue to operate reliably even when processes themselves degrade, such as with higher temperatures and vibration levels, as this would be pointless if the CBM itself required regular maintenance or had a limited lifespan. Therefore, CBM stability is crucial, especially ensuring that power converters are highly efficient, energy-efficient, and have low thermal stress to avoid shortening their lifespan.
RECOM's RxxCT(E)xx series is an isolated DC/DC converter with a high ambient temperature rating (up to 140°C), providing a 5V nominal input to a 5V or 3.3V output with power ratings of 0.5W or 1W, all within a slim SOIC-16 package. The output voltage is compatible with active sensors and microcontrollers or DSP front-ends for data analysis. Isolated CANBus™, MODBus™, or PROFIBus™ interface power supplies offer enhanced 5kVac isolation, while a basic isolated 3kVDC version is available for less demanding applications.
If the available power source varies greatly, such as solar power for charging batteries, a regulated DC output is required. Simple linear regulators are too inefficient and will quickly deplete the battery. In such cases, RECOM's R-78Exx-1.0 is an ideal switching regulator module, achieving up to 97% efficiency in outdoor solar applications and is also suitable for monitoring mobile devices, such as railway axle bearings.
Solutions for node power supplies using local AC power typically opt for small AC/DC converters, sometimes up to 277VAC. These can be used in conjunction with the upcoming IO-Link industrial sensor system, a digital bidirectional serial interface using a standard M12 connector. This system requires 24V and a maximum load of 410mA per node, so RECOM's onboard AC/DC converters from the RAC10, RAC20, and RACM40 series meet the 40W requirement for four IO-Link ports. The lower-power RAC03 series can also be used in wirelessly connected process controllers, enabling remote process control and signaling when temperatures exceed limits.
Gas leak, noise, and IR temperature sensors are typically mounted on the ceiling, with the lighting circuit providing a suitable AC power supply of 115V, 230V, or 277VAC, which is the phase voltage in a 480V three-phase system. Data transmission is performed using a remote LPWAN radio (LoRa, sigfox, KNX-RF, etc.) or a cellular network (5G, NB-IoT, GSM, etc.). Since power requirements are generally low, a RECOM universal input, 5W onboard RAC05-K/277 is a good choice.
To achieve autonomy, some sensor nodes can be powered by harvested energy, which could be solar, acoustic, radio frequency, vibration, or temperature gradients. The voltage source may be very low, thus requiring efficient power conversion to boost the voltage to a suitable level for the sensors and processor. The RECOM REH-3-31.8 module is designed for this purpose, operating at input voltages as low as 50mV while providing dual 3.3V/1.8V outputs. This component includes maximum power point (MPP) tracking for photovoltaic cells and can be coupled to batteries or supercapacitors for energy storage.
Condition monitoring is an ideal way to maintain process availability at minimal cost, while also easily integrating with IIoT. Reliable and economical power conversion is a key element of CBM, and RECOM's diverse product portfolio meets all application requirements.
