piezo waterproof vibration sensors for motors

Rotor imbalance (motor) Wheel imbalance (pump/fan) Misalignment Soft foot Looseness Bearing defects Rolling element Outer/Inner race Poor bearing assembly Lubrication (oil and grease).

Accelerate your digital transformation to compete more efficiently, generating higher productivity, improved asset uptime, increased safety, and lower costs.

In the world of Industry 4.0, many industrial manufacturers have or plan on implementing a digital transformation strategy that involves augmented machines with wireless connectivity and sensors, connected to a system that can visualize the entire production line, control, and make decisions on its own. This digital transformation describes the trend towards automation and data exchange in manufacturing technologies and processes.

At the epicenter of this transformation is Maintenance 4.0, a method of preventing asset failure by analyzing production and maintenance data to identify patterns and predict issues before they happen.

Over the decades, maintaining a competitive edge across asset-intensive industries required companies to adopt powerful efficiency tools and methodologies like Lean Six-Sigma and Total Quality Management. However, with the advancements in digital technologies and big data, many industry leaders are turning to ways to accelerate automation and data exchange to make better decisions.

Predictive Maintenance (PdM) is an essential part of many industrial companies’ 4.0 strategies. As a method of preventing unplanned downtime, by analyzing production and maintenance data, an organization can identify patterns and predict failure before it occurs. At the highest level of PdM maturity, companies are utilizing a combination of condition monitoring and process data for better detectability of hard to detect failure modes.

The “data silos” of condition monitoring data (e.g. vibration, ultrasound) and process data (e.g. flow, pressure, temperature) can be combined to obtain better detectability of hard to detect failure modes, which leads to earlier and better alarming. By embracing “the future of predictive maintenance” it’s possible to better support the decision-making process and predict previously unpredictable failures.