Process Improvement using engineering knowledge to reduce cost through increasing productivity of machines and men by reducing machine time and man time is the main activity of industrial engineering.
Many other disciplines help industrial engineers to achieve their objectives. But the main discipline that is the foundation for IE is engineering of products and processes.
Process chart can be prepared for equipment also. This is what was mentioned by an author. But one does not see examples of process charts with equipment as the focus.
Equipment can be under operation, breakdown/maintenance, setup, idle time due to lack of material, idle time due to lack electrical power, idle time due to lack of tools, idle time due to lack of operator etc. Hence, in normal process charts, temporary delays include delays due to equipment breakdowns. Individual equipment maintenance may need to be investigated under process improvement studies of various parts and assembly. Overall maintenance policies will affect or improve all processes.
The field of maintenance expanded so fast, and in smart plants, the outcome industrial revolution 4.0, predictive maintenance is an important activity. Development in predictive maintenance is far ahead of application of IoT in the manufacturing process. Industrial engineers have to keep track of developments in the maintenance field to utilize them as early as needed in their process improvement activity, process industrial engineering.
SOPs for Maintenance
2021
Checklist - Top Preventive Maintenance Tips for Your CNC Machine Tools
by Hwacheon Asia - Aug 6, 2021.
Preventive Maintenance Checklist
(Schedule regular maintenance or servicing sessions, and maintain documentation of service/maintenance. Also, have a specific area for your employees to report issues.)
MECHANICAL SYSTEM
Verify machine level
VMC – verify spindle sweep
Lathe – verify turret square to spindle face
Check lubrication is reaching the spindle
Inspect rotary oil level (mill)
Inspect drive belts
Verify gearbox/motor operation
Inspect tool changer and lubricate
Inspect spindle taper condition (mill)
Inspect toolholders and grease pull studs (mill)
Verify smooth turret indexing (lathe)
LUBRICATION SYSTEM
Inspect filter
Inspect hoses and fittings
Check pump operation / test
Check/adjust pressure, spindle regulator
Inspect system for leaks
Change gearbox oil (lathe only)
Change hydraulic fluid and filter
ELECTRICAL SYSTEM
Clean electrical cabinet
Check incoming voltages
Check DC bus voltage
Inspect connections and terminals
Inspect electrical ground connections
Replace or clean filter fan
Check safety locks
Check axis motors and cables
COOLANT SYSTEM
Inspect coolant hoses
Verify all pump operation
Clean coolant filters
Test and adjust coolant concentration
Inspect coolant tank for sediment
AIR & LUBRICATION SYSTEM
Inspect all lines and hoses
Verify all pump operation
Check incoming air supply
Verify lube/pump operation for axes
MECHANICAL SYSTEM:
Inspect tool holders/grease pull studs.
Inspect the rotary oil level (Mill)
ENCLOSURE/SAFETY:
Inspect safety interlock operation
Inspect windows for damage
Inspect way covers and wipers
PROBE SYSTEM:
Check the probe batteries
Check probe calibration
Monitoring the condition of the spindles: Sensemore uses Trigger device. Trigger sends the measurement order to the sensor by transmitting the 5V signals to the receiver with a code to be returned at the end of each process. Thus, it can monitor the vibrations of a CNC lathe in the grinding wheel spindle at the end of each machining and in a way that the parameters remain the same in every measurement.
Operating the grinding wheel spindle at a constant speed for a short time at the end of each machining and taking vibration measurements during this process will create a correct trend as it is within certain boundary conditions. The code added to the CNC lathe sends a 5V signal to the Sensemore Trigger device after each part is machined. Sensemore Trigger gives the measurement order by triggering the Wired with the signal it receives. The whole process is completed in as little as five seconds and analyzes are performed on the Sensemore cloud application.
Nowadays, maintenance, and quality processes are carried out in an automated manner. . Sensemore, thanks to the portable accelerometers, application-specific hardware, and cloud-based analysis program they developed, offer easier and more reliable automated maintenance and quality control processes.
Troubleshooting CNC Breakdowns and Steps to Avoid Them
March 22, 2021
Industry 4.0 in CNC Machine Monitoring
Predictive Maintenance Case Studies
June 2020
SAP Predictive Maintenance and Service
Combine sensor data with business information in your ERP, customer relationship management (CRM), enterprise asset management (EAM), and augmented reality systems using SAP Predictive Maintenance and Service, part of the SAP Intelligent Asset Management solution portfolio.
Cloud and on-premise deployment
Insight from sensor data
Prediction of equipment malfunctions
Optimized resource management
https://www.sap.com/hk/products/predictive-maintenance.html
Azure AI guide for predictive maintenance solutions
01/10/2020
42 minutes to read
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/predictive-maintenance-playbook
2019
Submitted on 12 Dec 2019
A Survey of Predictive Maintenance: Systems, Purposes and Approaches
Yongyi Ran, Xin Zhou, Pengfeng Lin, Yonggang Wen, Ruilong Deng
This paper provides a comprehensive literature review on Predictive Maintenance (PdM) with emphasis on system architectures, purposes and approaches. we first provide a high-level view of the PdM system architectures including the Open System Architecture for Condition Based Monitoring (OSA-CBM), cloud-enhanced PdM system and PdM 4.0, etc. Then, we make clear the specific maintenance purposes/objectives, which mainly comprise cost minimization, availability/reliability maximization and multi-objective optimization. Furthermore, we provide a review of the existing approaches for fault diagnosis and prognosis in PdM systems that include three major subcategories: knowledge based, traditional Machine Learning (ML) based and DL based approaches. We make a brief review on the knowledge based and traditional ML based approaches applied in diverse industrial systems or components with a complete list of references, while providing a comprehensive review of DL based approaches.
https://arxiv.org/abs/1912.07383Predictive Maintenance Services and Research from GE
Predictive Maintenance technologies aim to detect, diagnose, and predict failures and degradation in machine components prior to criticality. The ultimate goal is to prevent downtime, identify root causes for follow-up action, and enable efficient evidence-based maintenance planning and optimization. GE Research has a rich history in the development of Predictive Maintenance technologies, with deployed tools managing over one hundred thousand assets across the GE business units as well as countless more for GE's customers in the aerospace, power generation, transportation, oil exploration, and healthcare domains.
The primary research topics being pursued by GE fall into two categories: Early Warning and Prognostics.
Early Warning pertains to detecting anomalous behaviors in a system's operation at the earliest possible time to provide the maximum lead time for any potential action. This technology is applicable across industrial verticals and is traditionally the first item in combined Prognostics Health Management (PHM) deployments. GE Research software solutions for early warning are built around unsupervised, semi-supervised, and fully-supervised data exploration, enabling a broad span of solution complexities based upon the availability of data. Research has included an emphasis on fusion algorithms to integrate alerts and mixed-type information from multiple models to improve prediction accuracy and lead detection time as well as reduce alert fatigue. Similarly, research activities have yielded a robust multivariate time series search pipeline to speed up human interaction when searching for signatures, features, and patterns in massive time series data (for root cause and diagnostic reporting).
Prognostics go a step further to provide long term predictions of behavior and life. Prognostic algorithms aim to forecast remaining useful life, time to reduced capability, and emergent fleet segmentation for planning inspection, maintenance, repair, and spare parts inventory. GE Research technologies in this space are built upon a suite of hybrid modeling techniques that use embodied domain physics along with condition monitoring data from fielded systems and simulations. This also allows learning systemic behaviors from entire fleets through techniques such as transfer learning. Furthermore, auto-inspection technologies allow us to inspect and label component condition (and quantify performance of associated prognostic models) without human bias, adapting the fielded models in a continuous learning mode.
GE pursuing research in the area of Predictive Maintenance seeking new frontiers in the related technologies.
https://www.ge.com/research/project/predictive-maintenance
AWS Solutions Library - AWS Solutions Implementations - Predictive Maintenance Using Machine Learning
What does this AWS Solutions Implementation do?
Predictive Maintenance Using Machine Learning deploys a machine learning (ML) model and an example dataset of turbofan degradation simulation data to train the model to recognize potential equipment failures.
You can use this solution to automate the detection of potential equipment failures, and provide recommended actions to take. The solution is easy to deploy and includes an example dataset but you can modify the solution to work with any dataset.
https://aws.amazon.com/solutions/implementations/predictive-maintenance-using-machine-learning/
Strategic Maintenance Planning
Anthony Kelly
Elsevier, 28-Jun-2006 - Business & Economics - 304 pages
Strategic Maintenance Planning deals with the concepts, principles and techniques of preventive maintenance, and shows how the complexity of maintenance strategic planning can be resolved by a systematic ‘Top-Down-Bottom-Up’ approach. It explains how to establish objectives for physical assets and maintenance resources, and how to formulate an appropriate life plan for plant. It then shows how to use the life plans to formulate a preventive maintenance schedule for the plant as a whole, along with a maintenance organization and a budget to ensure that maintenance work can be resourced.
This is one of three stand-alone volumes designed to provide maintenance professionals in any sector with a better understanding of maintenance management, enabling the identification of problems and the delivery of effective solutions.
* The first of three stand-alone companion books, focusing on the formulation of strategy and the planning aspects of maintenance management
* Learn how to establish objectives - for physical assets and maintenance resources; Formulate a life plan for each unit and a preventive maintenance schedule for the plant as a whole; Design a maintenance organization and budget to ensure that the maintenance work can be resourced
* With numerous review questions, exercises and case studies - selected to ensure coverage across a wide range of industries including processing, mining, food, power generation and transmission
Ud - 6.5.2022, 4.10.2021, 15.9.2021, 24 May 2021
Pub on 6 July 2020
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