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Machine Diagnostics: Improving Maintenance Strategies to Improve Your Plant

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In 1943. C.H. Waddington, a scientist with the Royal Air Force, noticed that something was wrong with the RAF's maintenance strategy. After collecting and analyzing data on repairs, he discovered that breakdowns were more likely to happen after scheduled maintenance sessions. This phenomenon, which later became known as the Waddington effect, shows that preventive maintenance can often be more damaging than no maintenance, as it interferes with a satisfactory status quo.

We can attribute the invention of predictive maintenance to Waddington. Today, predictive maintenance holds a market worth $16 billion. And
despite predictive maintenance’s longevity, its growth has been boosted by Industry 4.0. That growth has yet to plateau.

At AiSight, we’ve developed a predictive maintenance solution that goes beyond anything Waddington might have dreamed of—into the realm of machine diagnostics. The AiSight solution not only predicts malfunctions long before they happen, it offers real-time monitoring of machine condition and root-cause analysis of faults, making it an asset in sound maintenance strategies.

Maintenance Strategies: From Reactive Maintenance to Machine Diagnostics

Maintenance strategies differ at the outset according to the metrics they monitor, such as sound, temperature and vibration. Vibration analysis, in particular, is a powerful way to assess the performance of industrial machines—changes in machine behavior almost immediately reflect in the vibration patterns emitted by the machines. However, such a deep level of monitoring is not yet common practice. Many operators still rely on older methodologies. The following outlines the most common maintenance strategies.

Reactive Maintenance

This maintenance strategy implies that intervention only happens whenever a machine breaks down, without executing prior analysis or optimization.

Preventive Maintenance

Preventive maintenance relies on routine maintenance interventions. During each control session, operators or service suppliers physically inspect the plant’s equipment. They may or may not detect a malfunction and intervene accordingly. However, as the Waddington paradox proves, this strategy is not always effective. Intervention often means interfering with a machine's normal operation, which can negatively affect its performance. Until now, this maintenance strategy, combined with reactive maintenance, was the standard.

Condition-Based Monitoring

Condition-based monitoring is a maintenance strategy based on the analysis of vibrations emitted by the machine. However, intervention is only performed if the signal exceeds a critical, predefined threshold value. This can indicate three different things:

a. The machine is not really broken. Since the threshold is fixed, there are no further insights on the meaning of the vibrations and the signal might have exceeded the threshold for other reasons.

b. There is a real malfunction but operators do not know what it is or how much time is left until failure. Once again, the vibration analysis is not thorough enough to provide that information.

c. There is a real malfunction, and operators know exactly what it is and how to fix it. However, they have to carry out their analysis and repairs in the high-stress, unplanned context of an emergency, stopping the machine mid-production.

Predictive Maintenance

Predictive maintenance goes a step further than condition-based monitoring by collecting a wide range of signals and using machine learning algorithms to analyze them. This makes it possible to gain additional insights about the machine and allows operators to schedule maintenance sessions around production, such as during planned shutdowns.

Machine diagnostics

Machine diagnostics actually go beyond predictive maintenance and represent the most advanced level of maintenance strategy. This maintenance strategy allows a thorough knowledge of the equipment and its parameters. It not only calculates the time to failure, it also gives more insights into the root causes of the error. Operators using machine diagnostics, therefore, gain a deeper understanding of why the error occurred and can easily initiate the right measures. This in-depth analysis is what the AiSight machine diagnostics solution can provide.

Why upgrade to predictive maintenance or machine diagnostics?

Improving your maintenance strategy has two main benefits: increased performance and decreased costs.

As reported in a survey, administered by the VDMA, of businesses that implement predictive maintenance, increased performance is an even greater advantage than the cost decrease. Increased performance mainly results from:

  • Longer machine availability
  • Longer useful life of the machine
  • Safer and more sustainable operations
  • Increased quality of the process and end product

On the other hand, decreased costs result from saving on the following:

  • Repairs and spare parts
  • Communication with service providers
  • Downsizing of service staff

Moreover, assessing whether you are allocating too many or too few resources to your maintenance strategy is very easy. The clearest measurement to do so is the ROI (return on investment), which demonstrates whether there is a balance between the expenses arising from machine repairs, and the money invested in the implementation of predictive maintenance or machine diagnostics solutions. We will write more on ROI in future articles.

Why aren’t machine diagnostics standard in maintenance strategies?

81% of German firms recognize predictive maintenance and machine diagnostics as important developments. But only 40% have used relevant offerings or technologies. If the benefits of implementing a predictive maintenance strategy are so widely acknowledged, then what is preventing more firms from adopting one?

One reason is that such a strategy is difficult to implement. To do so, a company would need the following: sensors, to detect the vibrations of your machine; a system that gathers and aggregates sensor data; a central hub, where all the data can be stored, safely; algorithms to analyze them; specialized, skilled people, to make the algorithms work and draw conclusions. All of these elements add up to additional costs.

Industry 4.0 delivers better maintenance strategies

Luckily, among the great innovations brought forth by Industry 4.0 come new solutions aimed at making the upgrade to predictive maintenance and machine diagnostics an attainable objective for all types of firms. Among these solutions is AiSight’s Aion sensor node.

The hardware component of AiSight’s machine diagnostics solution is a sensor node equipped with state-of-the-art vibration, magnetic field, and temperature sensors; Wi-Fi communication; and high-performance microcontrollers enabling pre-processing on the edge. The flexibility of the solution allows operators to easily install the sensor kit on a machine, using magnets or screws, and run it without having to undergo additional training. Data is collected by the sensor kit in real time and transmitted to the recently revamped dashboard. The information is then analyzed by machine learning algorithms and operators are alerted of any anomalies.

The broad spectrum of vibration signals that can be caught by the sensor node allows it to detect even the smallest deviations from the normal behavior of the machine. Our algorithms can simultaneously perform a root cause analysis to determine the cause of the deviation. By doing this, the sensor node will help ensure that the machine is operating efficiently—that's true machine diagnostics.

Click here to learn more about how this can reduce your plant’s costs.

If you want to know more about AiSight’s research, drop us an email!

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