When The Drive Provides Real-time Data Without Additional Sensors

Generating added value without incurring hardware costs should sound attractive to many OEMs. This is exactly what condition monitoring promises. The reason: No additional sensors are used. The highlight of the solution is to tap the added value of information from existing data sources. Lenze provides pre-tested algorithms for various applications and supports machine builders in converting their process know-how and machine knowledge into a utility-enhancing model for condition monitoring.

Additional information on the subject of what distinguishes condition monitoring from predictive maintenance

Condition monitoring and predictive maintenance are always used as synonymous terms, but there are two different concepts.

Predictive maintenance is the prediction of events or the probability of events, for example when the probability that a gearbox defect will occur in the next 50 hours of operation increases to over 90%. With such a forecast, you could plan to replace the gearbox in good time before the system actually fails.

Condition monitoring, on the other hand, is a preliminary stage that enables a more detailed description of the current status from the interpretation of existing data. This requires a deep understanding of machines and processes in order to generate meaningful information from "bare" data. Analyzes based on machine learning (ML) and AI can help to identify anomalies faster.

Example 2-axis robot

At SPS 2019, the company demonstrated this principle using a show case with two different approaches. One is model-based, here the measured actual values are compared with those that result from the assumed mathematical description of the machine. If certain tolerances are exceeded, this is interpreted as a fault.

The second approach is data based. An algorithm learns the behavior of the system and the mutual influence of the parameters, such as speed, acceleration, torque, position and current consumption. The real values are compared with this learned description to define deviations.

In the fair show case z. B. simulates increased friction on the spindle and wear on the belt drive. In both cases, the anomalies can be identified via current and torque values, be it by an absolute increase in the value or by abnormalities in the frequency analysis. In both cases, the condition monitoring alarm sounds and shows the causes on a dashboard.

Control or cloud?

The two condition monitoring approaches differ not only conceptually. The question of how the data is evaluated is also different. The model-based evaluation is usually carried out in the control system, since no high computing power is required. In contrast, ML and AI analyzes come into consideration for the data based evaluation, usually as a cloud application.

With its portfolio, Lenze gives the OEM freedom of choice. This includes a number of differently dimensioned PLCs for model-based condition monitoring. Data-based evaluation can also be carried out locally if the Cabinet Controller c750 is used. Alternatively, the gateway to the cloud is open with the gateway x500. Combined with the x4 platform, machine builders receive a turnkey cloud solution that, in addition to condition monitoring, also includes remote maintenance of the machine and user-friendly asset management.

Efficient condition monitoring

Efficient condition monitoring is based on the interpretation of information that is already available. No additional sensors are required, instead the machine devices work as sensors. With its extensive automation portfolio of hardware, software, network and cloud applications and the resulting know-how, Lenze can offer extensive assistance in interpreting data. At the same time, the manufacturer supports OEMs to become data scientists of their machines. (ud)

User meeting mechatronic drive technology

The focus of the user meeting mechatronic drive technology is on the mechanical components of gears, clutches and brakes as well as their design, dimensioning and interaction in the overall mechatronic system.

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