BehrTech Blog

Why Wireless Vibration Monitoring is Critical to Predictive Maintenance

It is estimated that industrial manufacturers incur a staggering $50 billion cost every year due to unplanned downtime, and maintenance expenses make up 15% to 40% of total production costs. These statistics make it clear why predictive maintenance is one of the most discussed topics in the Industry 4.0 era. The ability to predict and prevent serious failures ahead of time through a targeted approach promises to drastically improve equipment uptime while reducing maintenance costs.

Continuous condition monitoring has a central role to play in predictive maintenance practices, and a critical monitoring parameter is vibration. Equipment vibration uncovers important insights into multiple components issues that can reduce process quality and eventually, result in production downtime. Before we dive into the essentials of vibration monitoring, let’s take a quick look at what values predictive maintenance has to offer to modern industries.

A Snapshot of Predictive Maintenance in Industry 4.0

predictive maintenance
The impact of different maintenance strategies on asset uptime and Overall Equipment Effectiveness (OEE). Source: Deloitte

With the notion that unplanned downtime is much more expensive than planned downtime, preventive maintenance has been an industry standard for decades. Yet, redundant upkeep activities just for the sake of caution aren’t an optimal approach, either. A study has shown that as much as half of preventive maintenance cost is wasted, which causes a direct impact on the bottom line. Not to mention, only 20% of machine failures are age-related, while the other 80% are sporadic events. So, a routine-based maintenance scheme doesn’t necessarily guarantee that problems can be timely discovered and resolved.

Predictive maintenance overcomes these pitfalls through continuous monitoring and analysis of machine health to proactively diagnose and anticipate failures. As a core pillar in the Industry 4.0 revolution, predictive maintenance capitalizes on a new breed of IoT technologies to collect data on all happenings inside a machine. Marrying rich real-time and historical asset data with machine learning and predictive analytics, different breakdown types, alongside their root causes and preceding symptoms can be effectively studied and pinpointed. As soon as a red flag in equipment behavior arises, inspection and repairs can be scheduled to avoid catastrophic outages.

As opposed to its preventive counterpart, predictive maintenance draws on a wealth of data about actual asset performance rather than a speculated recurrent schedule. This eliminates excessive overhauls and the resulted downtime, while still ensuring seamless and reliable equipment operations for optimal production output. According to Deloitte, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25% on average. Proper maintenance also contributes to improving asset lifetime.

predictive maintenance

Vibration Monitoring for Predictive Maintenance

For rotating equipment that is widely used across industries, vibration is among, if not the top indicator for impending failures. Unwanted increase in vibration intensity produces detrimental forces to the components which jeopardize equipment lifetime and quality. Without timely intervention, asset failures and process shutdowns are inevitable. Such an increase is often a direct consequence of rotational part faults like:

  • Misalignment and unbalance
  • Mechanical looseness
  • Gear defects
  • Early bearing wear
  • Lack of lubrication
  • Cavitation
  • Resonance
  • Rubbing

Outfitting machines with sensors like accelerometers, allows manufacturers to stay on top of any alteration in vibration patterns. Continuous monitoring is greatly beneficial as vibration issues often don’t escalate and cause equipment damage right away. Instead, they progress slowly at the outset, and the vibration curve only turns steep when approaching to the failure point. By closely monitoring and detecting the trend at an early stage, technicians will have enough time to counteract before the breakdown happens.

Entering Wireless Vibration Monitoring

While vibration sensors aren’t new in industrial settings, older sensor types are typically wire-based, making the deployment process bulky and expensive. Their applicability is, therefore, usually limited to high-value, critical assets. Another approach to collecting vibration data relies on having technicians routinely take measurements using portable devices. Needless to say, this is time-consuming and labor-intensive, yet fails to provide seamless, ongoing insights into machine health. There’s a risk that problems could emerge during intervals between two inspections and be left unattended until damages are visible.

Enabling sensors with wireless connectivity promises to fulfill the unmet need of large-scale, remote vibration monitoring for predictive maintenance. In most scenarios, sensors only need to send vibration data every minute or every few minutes, so wireless communication provides a viable and far more cost-effective option than its wired counterpart. Leveraging ultra-low power technology like Low Power Wide Area Networks (LPWAN), your sensor network can be self-powered with independent batteries that can run for years, to drastically ease installation and maintenance. What’s more, long-range and sub-GHz radio link ensures reliable data transmission from distributed sensors at vast, metallic industrial environments, while avoiding interference from the congested 2.4 GHz band. It also enables a star-topology deployment that is much simpler to set up and manage compared to a mesh network.

As Industry 4.0 continues to progress, a predictive maintenance strategy allows manufacturers to make great strides in optimizing asset uptime and efficiency. In this context, wireless vibration monitoring empowers technicians with critical, actionable data about machine performance at unprecedented scale, to turn predictive maintenance into reality.

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