Analyzing Machine downtime

Introduction

• In Manufacturing,keeping machines running efficiently is essential for meeting production targets and maintaining day to day operations. By analyzing operational data, manufactures can identify patterns that lead to downtime and take proactive steps to prevent it.

• I focused on exploring and understanding machine performance data to uncover insights about what factors contribute to downtime.

overview.

• The company operates three different machines on its shop floor that produce different sized components, so minimizing the downtime of these machines is vital for meeting production deadlines.

💾 The data

• The company has stored the machine operating data in a single table, av Each row in the table represents the operational data for a single machine on a given day:
• "Date" - the date the reading was taken on.
• "Machine_ID" - the unique identifier of the machine being read.
• "Assembly_Line_No" - the unique identifier of the assembly line the machine is located on.
• "Hydraulic_Pressure(bar)", "Coolant_Pressure(bar)", and "Air_System_Pressure(bar)" - pressure measurements at different points in the machine.
• "Coolant_Temperature", "Hydraulic_Oil_Temperature", and "Spindle_Bearing_Temperature" - temperature measurements (in Celsius) at different points in the machine.
• Spindle_Vibration, "Tool_Vibration", and "Spindle_Speed(RPM)" - vibration (measured in micrometers) and rotational speed measurements for the spindle and tool.
• Voltage(volts) - the voltage supplied to the machine.
• Torque(Nm) - the torque being generated by the machine.
• Cutting(KN) - the cutting force of the tool.
• Downtime - an indicator of whether the machine was down or not on the given day.

Primary Objectives.

1. What is the first and last date readings were taken on?
2. What is the average Torque?
3. Which assembly line has the highest readings of machine downtime?
4. Explore correlations between the various operational data in the dataset.
5. Do you see a pattern in machine downtime over time?
6. Which factors (visually) seem to be connected to machine downtime?

Findings and insights

• ✅ Average spindle speed across all machines

select avg(spindle_speed_rpm)::numeric(10,2) as avg_spindle_speed
from machine_downtime;

• ✅ total machines in the industry

select count(distinct machine_id) from machine_downtime;

• ✅What is the first and last date readings were taken on?

select min(recorded_date) as first_date, max(recorded_date) as last_date
from machine_downtime;

• ✅ What is the average Torque?

select (avg(torque_nm))::numeric(10,2) as average_torque
from machine_downtime;

• ✅ Which assembly line has the highest readings of machine downtime?

select assembly_line_no,count(downtime) as total_downtime 
from machine_downtime
where downtime= 'Machine_Failure'
group by assembly_line_no
order by total_downtime desc;

select assembly_line_no,machine_id,count(downtime) as total_downtime 
from machine_downtime
where downtime= 'Machine_Failure'
group by assembly_line_no, machine_id 
order by total_downtime desc;