Electricity + Control March 2018

PLANT MAINTENANCE, TEST + MEASUREMENT

Advancing the Rotating Machine Industry to 4.0 Kegan Smith, PREI Instrumentation

We are in the 4 th Industrial Revolution where real time technology service and solu- tions enable companies to translate acquired data into actionable results.

Take Note!

Analysing and assessing rotating machinery such as compressor trains and blowers involves expe- rience and collection of the correct in-depth data. The ability to correlate this data with existing information coming from the machines’ control and process information is crucial to the effective- ness of such analysis. For analysing the ‘cause’ you require good quality high speed data collec- tion.

1

M ost companies and software developers have focused on the Process Product data alone. PREI instrumentation, Industry 4 Turbomachin- ery and Setpoint (B & K Vibro) have used high speed data collection and the power of OSI Pi to collect data from process, machinery, and critical control loops to monitor and analyse critical pro- cess machinery. The level of expertise required to analyse this rich data is not always available on iso- lated sites and plants; by leveraging cloud services and connected partnerships, Industry 4 Turboma- chinery can offer remote support and expertise in times of need or merely for routine reporting. Rotating machinery is still at the heart of all major processes; whether it be compressors or turbines. However, in this day and age, knowledgeable and ex- perienced personnel for this critical skill is scarce and reaching retirement age. The control, maintenance and monitoring equipment and staff for rotating ma- chinery are aged, and while the knowledge base is strong, much of the expertise remains in isolated departments with little unified migration to central- ised intelligent databases. The industry is being left behind in comparison to the tools available today. Industry 4.0 talks about a connected world; data streams coming from all corners of your environ- ment with intelligence built into the technologies to make our life easier. We are seeing a growth in the smart sensor and the interfacing networks that connect the ground layer instrumentation. Sensors and the interfacing systems with built in ‘intelligence’ give rise to localised and self-diag- nostics. This functionality is great for maintaining the health of the instrumentation and ideally suited for smaller repeatable balance of plant infrastruc- ture. However, for large integrated machinery and process trains there is no context given to the di- agnosis. That is to say, process instability can set off alarm bells pertaining to asset health indicators and vice-versa, giving a false sense of diagnosis

and leading you in the wrong direction. The next concern in a developing industry is convention; parties talking about the same observations but from different perspectives, or worse, people talk- ing about different observations but using com- mon terminology. I will refer to a diagram, the con- cept of which is not my own, but fast becoming a means of levelling the playing field.

2

3

The importance of good clean data

‘There was never a good knife made of bad steel’. Benjamin Franklin

The errors in data and its respective information content get compounded as one steps through the algorithms required to display, analyse and predict asset health. Let us consider the flow of information. We start at the source… the measurement of the correct in- formation, the reliability of the measurement, and the trust in that information once it is converted into recorded data. The quality of the instrumentation is crucial to the trust placed in the protection, control loops and analytics alike. The same is to be said for the sampling of the information. Resolution in meas- ured magnitude often surpasses the requirements, however poor time scales often lead to miss trust in the information. Firstly, any engineer who has per- formed a root cause analysis on data depicted as quantified step trends knows the frustration of allo- cating a cause and effect. Next, remembering that we are discussing industry 4 and the analysis of data across systems; time synchronisation of the various data streams needs to be maintained at a similar resolution to the sampled data. Having a 1 one sec- ond drift on data that is time stamped compared to a millisecond resolution is also problematic in assign- ing cause and effect (a problem that many are una- ware of, and think that the equipment is safe).

34 Electricity + Control

MARCH 2018