How to perform predictive maintenance of rolling mill gearboxes

Maximizing equipment performance and ensuring stable production are still the most urgent needs of iron and steel enterprises. The failure of the main line equipment of the rolling mill will seriously affect the operation efficiency and production safety of the rolling mill, and become a pain point in the process of "intelligent transformation" of steel enterprises.

Complex working conditions

Bearing cage prediction is even more difficult：

According to SKF statistics:

Since 2021, 92% of the rolling mill gearbox bearing failures diagnosed in the steel industry are bearing cage fractures (both with and without warning).

In the past 10 years, 57 typical gearbox failure cases were diagnosed and analyzed, and the bearing cage problem accounted for as high as 58%.

The main function of the bearing cage is to separate the rolling elements, evenly load and guide the rolling elements to operate normally. As a non-main stress-bearing part, its hardness and strength are much lower than other parts of the bearing.

（In steel rolling applications, complex working conditions such as alternating loads, abnormal shocks, and variable speed make the bearing cage extremely vulnerable.）

By analyzing the operational characteristics of rolling production, we found that implementing predictive maintenance of rolling mill gearboxes presents many challenges:

1.The source of the fault signal is difficult to distinguish

Affected by complex working conditions, the impact signal has a lot of interference; and the strength of the damaged signal is attenuated during the transmission process, and the received effective signal is very weak;

2.Image Most monitoring methods have low prediction accuracy

Most sampling techniques cannot ensure effective acquisition of cage fault signals, and cannot assess damage and the severity of damage;

3.Image Residual life cannot be estimated

The warning time of most analysis methods is usually very short, and it is impossible to interpret the deterioration trend and adjust the spare parts and maintenance strategy in time.

The rolling mill gearbox is the core equipment of the steel rolling production line. Bearing failure will cause serious consequences such as long-term downtime, huge loss of production, and surge in maintenance and repair costs.

Successful practice

Breaking the Predictive Maintenance Challenge：

In 2021, SKF successfully monitored multiple gearbox bearing cage failures on the 2050 hot rolling finishing mill gearboxes of two large domestic steel mill customers, helping customers reduce unplanned downtime and avoid failure expansion.

In the case of a steel plant, the SKF online monitoring system issued an abnormal early warning of vibration at the input shaft operation side of the F4 finishing mill, and reported the abnormal vibration induced by the fault characteristic frequency of the bearing cage of the gearbox.

(The main drive equipment of the rolling mill is equipped with SKF online monitoring system)

After the expert diagnosis confirmed the damage of the bearing cage, and gave suggestions on maximizing the remaining life time of the equipment, the equipment can continue to operate stably for about 4 weeks with the disease. Ensuring customers ample time to prepare spare parts before scheduled downtime for bearing replacement.

Take precautions

There is a recipe for predicting in advance：

1.Observe details and capture valid signals

The quality of data is a prerequisite for accurate monitoring.

Based on comprehensive consideration of the process characteristics, equipment structure, equipment load and force, and typical equipment failure modes of steel rolling production, the SKF rolling mill gearbox predictive maintenance solution has the following three application characteristics:

✓ The front sensor is closer to the load bearing area of the bearing

✓ The data sampling time of the hardware acquisition unit is highly synchronized with the rolling production

✓ Isolation of shock signals during rolling production

Ensure that the signal acquisition process of the rolling mill gearbox is efficient and the signal accuracy is high, and effectively deal with the impact of low-speed speed change and load change of the gearbox and impact signal interference.

                           （SKF data acquisition unit IMx-8）

2.In-depth analysis to improve prediction accuracy

SKF's proprietary acceleration envelope technology (gE), by effectively filtering low-frequency and high-amplitude vibration components, utilizes a complete bearing database and powerful damage frequency calculation capabilities, combined with professional monitoring solutions based on rolling production, to efficiently capture occurrences Transient shock signal in the early stage of bearing failure, and can identify bearing cage damage earlier and make accurate predictions.

（SKF acceleration envelope bearing warning precedes vibration velocity）

3.Professional diagnosis, estimated residual life

In addition to accurate predictions, it is also critical for steel mills to estimate equipment residual life. Relying on the rich experience of diagnostic analysis experts, SKF can effectively track the deterioration trend of bearing cage damage by using the Protean diagnostic rules in the system analysis software. Combined with site noise and production load, SKF can confirm the severity of cage fracture and evaluate the residual life of equipment. And develop spare parts and maintenance strategy.

                                          （SKF Protean Diagnostic Trend Alert） 

SKF's intelligent monitoring system makes the potential risks of equipment nowhere to hide, uses data to guide production, helps steel mills perform better predictive maintenance, and protects the safe and stable operation of production at all times.

In the future, SKF will continue to empower metallurgical enterprises to accelerate the transformation and transformation of "intelligent manufacturing" through smarter and cleaner methods, and move towards the goal of high-quality development.

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