The target and vision of the German federal government to achieve full networking of pre-sales, production and after-sales must, for complex products, necessarily start with the production process itself. At the operative level of the production plant, linking machine condition parameters and process parameters, monitoring these continuously and preparing meaningful conclusions are decisive for product quality and plant efficiency.

One component of this control circuit is an intelligent Condition Monitoring system that offers the flexibility required to accommodate a wide range of production plants and features the interfaces needed for smooth information exchange between the plant control system, process visualization unit and operator. The functioning principles of a monitoring system that meets these requirements are described below and illustrated in practical examples.

Industry 4.0 and the Monitoring Strategy

The description by the German Federal Ministry of Education and Research (BMBF) of the Industry 4.0 project and intelligent monitoring states the following (1):

– Along with increased automation in industry, the development of intelligent monitoring and autonomous decision-making processes is particularly important in order to be able to steer and optimize both companies and entire value-adding networks in almost real time.

– On the basis of detailed production data recorded online combined with intelligent visualization, control systems should be able to support decision-makers in planning and controlling production to considerably improve corporate goals, such as delivery reliability.

Requirements for an Intelligent Condition Monitoring System

Implementing the conceptual requirement for intelligent Condition Monitoring represents a certain challenge. Apart from purely technical demands, economic aspects must also be considered since the investment initially does not yield monetary benefits from the point of view of the business administrator.

To be able to collect reproducible and plausible monitoring data, control parameters must be synchronously correlated with the measurement data and flexibly classified into certain operating states.

Examples are varying load conditions and speed changes controlled by frequency converters in highly dynamic, controlled motion sequences. Without information on these processes and classification of measurement data into different load or speed classes, reliable interpretation of the monitoring data is only conditionally possible.

Particularly in failure-critical plants all process-related condition parameters should be recorded without delay and flow into the control system in almost real time. Classical monitoring systems with multiplexer scanning are not suitable here. Rather, synchronously measuring online systems with a time-based correlation of all incoming parameters and a fieldbus interface for rapid data exchange are called for.

Certain condition parameters that are subject to multiple influences should be recorded online with a high degree of detail. Frequency-selective monitoring of vibration-related condition variables provides significantly higher information content. Depending on the rotational kinematics high scanning rates may be required. This paired with the required synchronicity and the phase information results in high demands on the monitoring system in terms of computing power and data processing.

The following figures 1 and 2 illustrate how information is lost when discrete time windows are recorded (Fig. 1) compared to a continuous time window with overlapping and continuous data logging. Permanent monitoring, which necessarily generates vast amounts of data, is used on turbo rotors for machine protection monitoring, but is also useful for the highly dynamic motion sequences.

Fig. 1: Discontinuous data recording with a time gap (top) and continuous data recording with a sliding window (bottom).

The analytical evaluation of the monitoring data with variable reporting tools requires server capable diagnosis software. In parallel to this offline platform, a real-time visualization software with trending and interactive functions is required. It must support mobile terminals.

Fig. 2: Offline analysis.

Fig. 2 shows multiple frequency spectra (FFT) in a waterfall diagram. This offline display is generally used to study special frequency-selective phenomena. A typical example is structural resonance, and it is only called up when required. Real-time monitoring usually relies on trend monitoring of broadband characteristic values, as shown in Fig. 3.

Fig. 3: Real-time visualization with Online View 4.0.

Challenges in the Field for Condition Monitoring

Conveyors are an important element of a production chain and malfunctions can lead to costly production downtime. Typical industrial examples are in automotive or other construction assembly lines, conveyor belts and container cranes.

Fig. 4: Container crane with winch drive.

Short movement cycles with a variety of positions and load conditions are a challenge to obtaining reproducible and classified monitoring data. The high complexity of such monitoring tasks is illustrated here using the cable winch drive of a container crane as an example.

Fig. 5: Plot of torque load.

Fig. 5 shows the size of container crane drive units. The cable winch drives shown at the bottom right are located in the control room.

The goal of the operator is to identify anomalies in the drive components early on and monitor these components in terms of load and activation. The monitoring tasks in detail are:

• Torque load of the drive shaft of the cable spool
• Force applied by the service brake
• Optimization of the actuation of the motor/service brake
• Condition parameters of the gearbox/roller bearings (specific frequencies/housing temperature)
• Cloud-based remote monitoring

This application places requirements on the monitoring system that conventional systems can hardly meet:

• Actuation (start, load, direction of rotation):
• Flexible trigger processing
• Fieldbus/analogue
• Motion dynamics (startup/stop)
• Order tracking
• Operating states
• Extremely low-speed machine
• High scanning rate
• Large data volume

Fig. 5 shows a plot of the drive speed of the cable winch motor and the torque load of the drive shaft against time. Ideally, the main brake should release immediately when the motor is started up to prevent undue loading of the drive shaft. However, a zoomed analysis of the trend shown above reveals that there is a small time offset between the starting up of the motor and releasing of the brake, which requires adjustment of the actuation.

The newly developed VIBGUARD system delivers a real-time continuous and detailed condition overview of the relevant machine components with a focus on rotating parts. By synchronously and continuously gathering data from all analogue channels, VIBGUARD delivers time-correlated relationships between the machine components and process parameters. For example, it can provide information on how the smoothly running characteristics of a shaft change with temperature.

Fig. 6: Synchronous online Condition Monitoring System (CMS), stationary and portable versions

Fig. 6 shows two variants of the VIBGUARD online system: a board for stationary online applications and an industrial case with an integrated PC for mobile trouble-shooting.

Big Data and Pertinent Information

Total networking to create a smart factory throws up the topic of Big Data. Without intelligent data reduction, a monitoring system of the performance class described above alone generates information in the gigabyte range in a matter of a few weeks.

So-called expert systems try to deliver precise information from an abundance of data using condition patterns and a statistical approach. These expert systems are not universally applicable to the condition monitoring of production plants and therefore only partially suitable.

The likelihood of reliably predicting the failure of a certain component is still a matter of chance. A significant obstacle to creating a smart factory is networking, but handling, correlating, and correctly and automatically interpreting the data volume are of concern as well.

Maintenance specialists can only make the best use of the wear reserves of machine components if they obtain precise and compact information on the plant. System manufacturers and research institutes have been working on a universal and practicable expert system for some time. However, a solution is not yet within sight, and control circuits will continue to rely on the human factor for some time to come.

Christian Kaps

Prüftechnik Condition Monitoring GmbH,

christian.kaps@pruftechnik.com

References: 1. Bundesministerium für Bildung und Forschung (2014), Industrie 4.0-Innovationen für die Produktion von morgen (German Federal Ministry of Education and Research (2014), Industry 4.0 Innovations. For detailed information regarding VIBGUARD please visit: www.pruftechnik.com